Unconscious influences on decision making

Transcription

BEHAVIORAL AND BRAIN SCIENCES (2014), Page 1 of 61
doi:10.1017/S0140525X12003214
Unconscious influences on decision
making: A critical review
Ben R. Newell
School of Psychology, University of New South Wales, Sydney 2052, Australia
ben.newell@unsw.edu.au
http://www2.psy.unsw.edu.au/Users/BNewell/Index.html
David R. Shanks
Division of Psychology and Language Sciences, University College London,
London WC1H 0AP, United Kingdom
d.shanks@ucl.ac.uk
http://www.ucl.ac.uk/psychlangsci/research/CPB/people/cpb-staff/
d_shanks
Abstract: To what extent do we know our own minds when making decisions? Variants of this question have preoccupied researchers in a
wide range of domains, from mainstream experimental psychology (cognition, perception, social behavior) to cognitive neuroscience and
behavioral economics. A pervasive view places a heavy explanatory burden on an intelligent cognitive unconscious, with many theories
assigning causally effective roles to unconscious influences. This article presents a novel framework for evaluating these claims and
reviews evidence from three major bodies of research in which unconscious factors have been studied: multiple-cue judgment,
deliberation without attention, and decisions under uncertainty. Studies of priming (subliminal and primes-to-behavior) and the role
of awareness in movement and perception (e.g., timing of willed actions, blindsight) are also given brief consideration. The review
highlights that inadequate procedures for assessing awareness, failures to consider artifactual explanations of “landmark” results, and a
tendency to uncritically accept conclusions that fit with our intuitions have all contributed to unconscious influences being ascribed
inflated and erroneous explanatory power in theories of decision making. The review concludes by recommending that future
research should focus on tasks in which participants’ attention is diverted away from the experimenter’s hypothesis, rather than the
highly reflective tasks that are currently often employed.
Keywords: awareness; conscious; decision making; deliberation; intuition; judgment; perceptual-motor skills; unconscious
1. Introduction
Psychology is concerned with understanding how the mind
controls and determines behavior. Fundamental to this
goal is whether unconscious influences play a significant
role in the generation of decisions and the causation of behavior generally. Everyday notions such as “gut instinct”
and “intuition” capture the idea that subtle influences
falling outside awareness can bias behavior. Claims that
“People possess a powerful, sophisticated, adaptive unconscious that is crucial for survival in the world” (Wilson 2002,
p. vii) and that we should think less rather than more about
complex decisions (Dijksterhuis et al. 2006b) have a strong
grip on both theoretical perspectives and the public imagination (e.g., Gigerenzer 2007; Gladwell 2005; Lehrer 2009).
This article evaluates a wide range of research findings from
the past 20 or so years that have contributed to the development of this perspective.
The unconscious has of course played a major role in the
history of psychology, certainly predating Freud’s comprehensive development of the concept. But in the past few years it
has been the focus of extensive research in mainstream experimental psychology, including cognition, perception, and social
behavior, as well as in cognitive neuroscience, behavioral
© Cambridge University Press 2014
0140-525X/14 $40.00
BEN R. NEWELL is an associate professor of Cognitive
Psychology at the University of New South Wales in
Sydney. He has published more than 60 journal articles
on several aspects of higher-level cognition including
heuristic judgment, decisions under risk and uncertainty, categorization, learning, memory, and induction.
He has an enduring interest in the roles played by
implicit and explicit processes in all of these areas. In
2012 he was awarded an Australian Research Council
Future Fellowship.
DAVID R. SHANKS is a professor of Psychology at
University College London and head of the Division
of Psychology and Language Sciences. He is the
author of more than 100 publications in human cognitive and experimental psychology, including research
on learning and memory, causal inference, and judgment and decision making, and has authored or edited
seven books, including Straight Choices: The Psychology of Decision Making (with Ben Newell and David
Lagnado). Development and testing of computational
models is a major theme throughout his research. In
2011 he was awarded the Experimental Psychology
Society Mid-Career Award.
1
Newell & Shanks: Unconscious influences on decision making
economics, and other domains. Our focus is on the core
process of decision making, which relates to all of these areas.
In this article we take decision making to refer to the
mental processing that leads to the selection of one among
several actions (choices). Construed this way, we exclude
examples such as neurons or brain networks making
“decisions,” and we do not consider the visual system’s computation of low-level properties to be decision making. We
view consciousness as a property of individuals and hence
do not believe it serves any useful purpose to ask whether
area V5’s computation of motion, for example, is or is not
conscious. (It is, in contrast, perfectly reasonable to ask
whether an individual’s judgment of motion is conscious).1
The outline of the article is as follows: We begin by describing a framework for illustrating how unconscious processes
could be causally effective in decision making (as defined
above). We then articulate some of the requirements for an
adequate test of awareness and discuss the legacy of Nisbett
and Wilson’s (1977) highly influential work. The body of the
article reviews three major areas of research from the
decision-making tradition in which unconscious factors have
been studied: multiple-cue judgment, deliberation without
attention, and decisions under uncertainty. A final section considers research from the priming literature, both subliminal
priming and the so-called primes-to-behavior studies that are
prevalent in social cognition (e.g., Bargh et al. 1996). Although
few of these studies relate specifically to decision making, they
are provocative illustrations of possible unconscious influences
on behavior and thus warrant consideration in our review.
We do not, however, claim to offer a comprehensive literature review of all the research domains relevant to our guiding
question. In particular, we give only very brief consideration
(in sect. 6) to the literature investigating awareness of decisions
about movements (e.g., Libet 1985), illusory conscious will
(e.g., Wegner 2004), and neuroscience phenomena such as
blindsight (e.g., Weiskrantz 1986). Restricting our focus of
course leaves us open to the criticism that we are “looking in
the wrong place” for the evidence. Our response would be
twofold: First, pragmatic considerations make it impossible
to consider all the evidence in a single article, but we
contend that the areas we selected have been highly influential
in bolstering claims for unconscious decision making. Second,
the areas we focus on in the core of the review are those that
are most readily identified as involving decisions in the sense
defined above. In the motor-movement and neuroscience
domains, the nature of the decision being made and the information relied upon to make that decision are, arguably, less
well defined in the first place, thus making discussions of
peoples’ awareness of them that much more difficult. We
expand on these issues further in section 6.
Our critical analysis points to a surprising conclusion,
that there is little convincing evidence of unconscious influences on decision making in the areas we review, and that,
as a consequence, such influences should not be assigned a
prominent role in theories of decision making and related
behaviors. This conclusion is consistent with the view that
conscious thoughts are by far the primary driver of behavior
(Baumeister et al. 2011) and that unconscious influences –
if they exist at all – have limited and narrow effects.
1.1 A framework for the components of decision making
Our first step in examining the role of the unconscious in
theories of decision making is to propose a framework for
2
BEHAVIORAL AND BRAIN SCIENCES (2014) 37:1
thinking about how decisions could be influenced by
unconscious processes. The framework is based on the
lens model (Brunswik 1952), popularized in the judgment
and decision making field by Hammond, Stewart, and
many others (for overviews, see Hammond & Stewart
2001; Karelaia & Hogarth 2008).
The basic premise of the lens model is that a decision
maker views the world through a “lens of cues” that mediates between a stimulus in the environment and the
internal perceptions of the decision maker, as shown in
Figure 1. The double convex lens in the center of the
diagram shows a constellation of cues that diverge from a
criterion or event in the environment (left side of figure).
The decision maker uses these cues to achieve (e.g., correctly estimate) the criterion, and so these cues are
shown as converging (right side of figure) on a point of
response or judgment in the mind of the decision maker.
The lens model conceptualizes decision making as being
guided by judgment (see note 1). An application of the
lens model in the domain of medical diagnosis (e.g.,
Harries et al. 2000) would construe the physician as
attempting to decide on the best treatment (the judgment)
for a patient by determining the likelihood of a disease (the
criterion) given the symptoms (cues) relied upon in making
the judgment.
Figure 1 identifies five points (labeled A–E) at which an
unconscious influence might be exerted on decisions. Point
A captures the idea that an event or criterion in the
environment that is not consciously perceived by the
decision maker nonetheless influences behavior. An
example might be lack of awareness of the feedback from
making a correct or incorrect diagnosis. Point B is lack of
awareness of contingencies or relations between consciously perceived cues and the criterion or outcome. The
idea here is that there are properties of the stimulus
environment (termed “ecological validities”) that reliably
predict a criterion, but that the individual might be
unable to report or describe these relationships. For
example, a doctor might be unaware that certain consciously perceived symptoms are predictive of an illness
(e.g., Crandall & Getchell-Reiter 1993). A lack of awareness of the cues relied upon to make a judgment or decision
is illustrated by Point C in the figure. For example, a diner
might be unaware that the relative position of an option on
a menu influenced his choice (Dayan & Bar-Hillel 2011);
relative position in this scenario is simply not registered
in consciousness. The difference between B and C is
subtle: In one case (C) it is unawareness of a cue,
whereas in the other (B) it is unawareness of the ecological
or predictive validity of the cue. (Arguably, lack of awareness of a cue entails lack of awareness of its validity,
hence cases of unawareness at C entail unawareness at B
as well.)
Point D refers to a lack of awareness of one’s utilization
of cues. A doctor, for example, might appropriately base his
or her diagnosis on features present in a mammogram, and
might be aware of the features, but be unaware or mistaken
about how he or she incorporates those features into his or
her decision. The doctor might, for instance, be unaware of
a complex non-linear rule he or she is tacitly employing to
integrate information conveyed by the cues. Unawareness
of cues (C) also entails unawareness of one’s utilization
(D) of those cues. Finally, Point E indicates lack of awareness of choosing or making a judgment. Consider a lawyer
Figure 1. A lens model framework illustrating possible loci of unconscious influences on decision making.
who uses the right of peremptory challenge against a potential juror, based on an unconscious judgment or stereotyping of the juror as racially biased.
Our use of the lens model as a framework is illustrative,
and there are other formal frameworks such as signal detection theory and sequential analysis (see Gold & Shadlen
2007) for conceptualizing the elements of decision
making.2 Nonetheless, it affords some structure for evaluating the major areas of our review. Before turning to these
areas, however, in the next section we outline a set of criteria that further help to evaluate possible unconscious
influences on decision making.
1.2 Criteria for the assessment of awareness
Research on the role of awareness in decision making typically (but not invariably) seeks to contrast two types of
measurement, one being some behavioral index of performance and the other being an awareness assessment
based on the individual’s report, verbal or otherwise. An
unconscious influence on decision making is inferred if
performance is affected by some cue or factor that is not
reflected in awareness. Underlying theoretical constructs
are not the same as the measurements that we take of
them, and this is as true of awareness as it is of any other
psychological construct. It is therefore essential to recognize that an assessment of awareness will only be informative if it is relatively free from bias and error.
The criteria that need to be met by adequate awareness
measures have been the subject of extensive previous
discussion (e.g., Dawson & Reardon 1973; Ericsson &
Simon 1980; Lovibond & Shanks 2002; Shanks & St.
John 1994). In brief, the more reliable, relevant, immediate, and sensitive an awareness assessment is, the less
3
Table 1. Criteria for adequate assessments of awareness
Criterion
Explanation
Reliability
Assessments should be
unaffected by factors that do
not influence the behavioral
measure (e.g., experimental
demands, social desirability).
Assessments should target only
information relevant to the
behavior.
Assessments should be made
concurrently (so long as they
do not influence the
behavior) or as soon after the
behavior as possible to avoid
forgetting and interference.
Assessment should be made
under optimal retrieval
conditions (e.g., same cues
are provided for measuring
awareness as for eliciting
behavior).
Relevance
Immediacy
Sensitivity
likely it is to be distorted by bias or error. Table 1 provides
brief explanations of these criteria. As we shall see, many of
these criteria are not met by studies claiming to show
unconscious influences on behavior.
The relevance criterion (called the “information” criterion by Shanks & St. John [1994]) merits further consideration. Although it may seem obvious that, in order to
be suitable, an awareness assessment must target information that is relevant to the decision, experimental tasks
often prompt violations of the criterion. A case in point
arises in situations in which the researcher embeds a rule
in the experimental materials and asks whether unconscious acquisition of this rule can influence behavior (see
Fig. 1, Point B). Examples include artificial grammar learning (in which participants study strings of items that are
constrained to follow certain transition rules, e.g., Pothos
2007) and invariant learning (in which structural relations
govern the permissible stimuli). Much of the implicit learning literature is predicated on the idea that participants can
often respond on the basis of such rules without being able
to describe them. It is very tempting for researchers to
assume that participants’ task performance must be based
on an abstraction of the underlying rule governing the
structure of the stimuli (e.g., Marcus et al. 1999). Yet
numerous studies (e.g., Brooks & Vokey 1991; Johnstone
& Shanks 2001; Newell & Bright 2002) have documented
how performance in these tasks can often be more appropriately explained via learning of entire stimulus configurations together with similarity-based decision making, or
in terms of learning micro-rules. In such cases, the fact
that participants may be unable to report the rule does
not mean that it is unconsciously influencing behavior: To
claim otherwise is to violate the relevance criterion.
A further issue in regard to the relevance criterion concerns the influence of distal versus proximal cues on
decision making.3 The key issue is to what extent people
are unaware of the information that is triggering their
4
decision at the point of choice (proximal cues), as compared
to information in the past (distal cues) that might have
caused the current information (thoughts) to be present
at the point of choice. Consider a situation in which some
distal cue (your mother advised you as a child that
spinach is a good source of iron) caused a proximal cue
(your current belief that spinach is healthy), which in turn
influences a current decision (to select spinach off the
menu). Even though you might be unaware of the distal
influence on either your current belief or your decision,
you might be perfectly able to justify your decision in
terms of your proximal belief. Under such circumstances
it is plainly inappropriate to claim that the decision is influenced by an unconscious factor.
There are, in summary, a number of important criteria
that must be met in the design of an adequate awareness
assessment (see Table 1). Although these requirements
are extensive, it is important to note that the criteria are
not unrealistic or unattainable. Some of the studies
described at length below took considerable pains to deal
with these issues of awareness measurement, by measuring
awareness concurrently with performance (e.g., Lagnado
et al. 2006) or via multiple convergent questions (Maia &
McClelland 2004) or by employing nonverbal performance
measures assumed to index awareness (e.g., wagering:
Persaud et al. 2007), using questions that are reliable, relevant, and sensitive. We do not believe that these criteria
set the bar too high for assessing whether an influence is
unconscious. The criteria do not force researchers to
employ qualitatively different forms of assessment,
merely to use standard ones in a more careful way with
due recognition to the fine details of the experimental
task and its demands.
1.3 The legacy of Nisbett and Wilson
To a considerable extent, the willingness of contemporary
experimental psychologists to embrace the possibility of
unconscious influences on behavior can be traced to the
highly influential work of Nisbett and Wilson (1977).
Nisbett and Wilson launched a powerful series of arguments that people typically lack insight into their own
mental processes. Key among their claims were (a) that
people often misreport causal influences on their behavior,
falsely reporting factors that did not in fact influence their
performance and failing to acknowledge factors that truly
were causal, and (b) that people are rarely any more accurate in explaining their own behavior than outside observers
are, prompting the famous conclusion that “if the reports of
subjects do not differ from the reports of observers, then it
is unnecessary to assume that the former are drawing on ‘a
fount of privileged knowledge’” (Nisbett & Wilson 1977,
p. 248). When people do give veridical reports, it is
because they make use of a priori implicit theories about
causal relationships between stimuli and responses, rather
than because they have privileged conscious access to
their own mental processes.
We will not extensively review the evidence that has
accumulated on these issues since Nisbett and Wilson’s
(1977) article was published (for relevant discussions, see
Adair & Spinner 1981; Ericsson & Simon 1980; Smith &
Miller 1978; White 1980; 1988). However, we will
mention two significant challenges to Nisbett and
Wilson’s (1977) viewpoint. First, a number of their
demonstrations under (a) above fail to meet our criteria
regarding adequate assessments of awareness (see
Table 1). Consider an experiment in which participants
chose between (and justified their choice from) four consumer products that were in reality identical. Nisbett and
Wilson (1977; more details of the original experiments
are given in Wilson & Nisbett 1978) found that participants
tended to select the right-most of four alternatives (e.g.,
pairs of stockings) but did not mention position when justifying their choice, or flatly denied being influenced by position when asked directly (this would be an example of
unawareness located at Point C in the lens model of
Fig. 1). Instead, participants mentioned attributes such as
the quality of the stockings. The problem with this
finding is that asking participants about position fails the
relevance criterion, as position is almost certainly not a
proximal cause of choice (this argument was originally
made by Smith & Miller 1978). It is at best a distal cause
whose influence is mediated via the participant’s true
decision rule.
In such sequential choice situations, people tend to study
the options one at a time, usually (but depending on
culture) from left to right (Wilson & Nisbett [1978] confirmed that this was the case in the experiment). Suppose
that the decision rule is that if the current item is no
worse in terms of quality than the previous item, then
prefer the current item. After the initial item, each subsequent one is mentally compared with its predecessor
(Li & Epley 2009; Mantonakis et al. 2009), and because
the items are identical, the resulting final choice is the
right-most pair of stockings. Even though the rule may
lead (wrongly) to the belief that one item is superior to
the others, the choice is in no sense determined by
spatial position. Spatial position only has an influence
insofar as it affects how the items are sequentially
sampled. Indeed, under such circumstances it is perfectly
correct for participants to report quality as the basis of
their decision, as their decision rule incorporates judgments of quality, and to deny being influenced by position.
To establish that the choice is being driven by unconscious
influences, it would be necessary to show that participants
deny employing a sequential comparison process, but this is
not what Nisbett and Wilson (1977) asked their participants. Claiming that their participants were unconsciously
influenced by position is like claiming that an individual
who chooses the apartment she saw on Thursday, after
seeing others on Monday, Tuesday, and Wednesday, is
unconsciously influenced in her choice by the day of the
week.4
The second way in which subsequent research challenges Nisbett and Wilson’s (1977) position is equally
damaging. It appears far too strong to claim that individuals’ responses can be predicted just as well by observers,
who have access to nothing more than the public features
of the stimuli and context, as they can be by the individuals’
own verbal reports on their mental processes. Apart from
raising a number of serious methodological problems
with Nisbett and Wilson’s original studies (e.g., Guerin &
Innes 1981; Smith & Miller 1978; White 1980), later
research has clearly shown predictive advantages for
actors over observers (Gavanski & Hoffman 1987; White
1989; Wright & Rip 1981). It is apparent that in many
of the sorts of situations cited by Nisbett and Wilson, we
do in fact have introspective access to our conscious
mental states, and the verbal reporting of these states
conveys privileged information about the causes of our
behavior.
Having provided a framework for thinking about how
unconscious processes might influence decisions, and
having articulated some of the requirements for an adequate test of awareness, we now turn to three major
areas in which unconscious factors have played a prominent
role.
2. Unconscious influences in multiple-cue
judgment
Research into multiple-cue judgment focuses on situations
in which people attempt to predict an environmental criterion on the basis of imperfect probabilistic indicators –
just as a doctor might try to diagnose a disease on the
basis of symptoms, medical history, and results of diagnostic tests. A long-standing question in this field is the extent
to which such judgments are based on explicitly available
knowledge. This question is of psychological importance
because if “experts lack self-insight into the processes
underlying these judgments, they may be unconsciously
biased” (Evans et al. 2003, p. 608). This section investigates
this claim first by reviewing evidence relating to the development of self-insight in novices learning experimental
multicue judgment tasks, and second by examining the literature on the self-insight of experts performing real-world
multiple-cue judgments.
Following the pioneering work of Hammond and colleagues (see Hammond & Stewart 2001), many studies in
this area have employed the lens model framework of
Figure 1 to examine judgment. In a standard study participants make judgments about a series of “cases” (e.g.,
patients) for which information is available from a set of
cues. Multiple linear regressions are then performed
from the judgments to the cues to measure the “policies”
that judges adopt. The beta weights obtained from these
regressions give an indication of the cues that influenced
the judge, as well as the relative extent of this influence.
These beta weights are described as the implicit or tacit
policy underlying judgment (indicated on Fig. 1, Point D,
as cue utilizations).
To examine the extent of insight into judgments, these
implicit policies are then compared with self-assessments
of the importance of cues for determining judgments.
Importance can be assessed in a variety of ways, such as
asking judges to divide 100 points between the cues, with
higher numbers indicating greater reliance on a cue. The
strength of the correlation between these ratings of importance and the beta weights derived from multiple
regression is taken as indicating the extent of insight. A
widely accepted consensus from this research is that
there is often a lack of correlation between the two
measures of the usage of cues, reflecting judges’ poor
insight (Arkes 1981; Evans et al. 2003; Slovic & Lichtenstein 1971).
2.1 Examining insight in novice judges
According to some researchers, the reason for this poor
insight is that judges learn how to make their judgments
in an implicit manner (e.g., Evans et al. 2003), and these
5
processes are thus inaccessible to introspection. Testing
such an account in established experts is of course difficult
because the relevant learning has already been accomplished. Thus researchers have studied the acquisition of
judgment policies in laboratory analogues of typical realworld judgment tasks.
An illustrative study is that of Evans et al. (2003) who
asked participants to predict the suitability of fictional job
candidates for an unspecified job on the basis of “ability
tests.” The complexity of the task was manipulated by
varying the ratio of relevant and irrelevant ability tests. Relevant tests contributed a constant value (+1 or −1) to the
linear model that determined feedback; irrelevant tests
contributed zero. Participants learned over a period of 80
to 100 trials with corrective feedback and were then
given 40 test trials in which no feedback was provided. At
the end of the test, participants rated each test on a scale
from 1 (less relevant) to 7 (more relevant).
Evans et al. (2003) assessed “implicit” knowledge by
measuring participants’ revealed beta weights from test
judgments and “explicit” knowledge by calculating a difference score between ratings given to relevant and irrelevant
cues. In their second experiment, Evans et al. claimed to
find a dissociation between these two measures of knowledge. Cue polarity (positive/negative) and absolute cue
number (4 or 6) had large effects on the self-insight and
performance scores (correlations between the criterion
and prediction – labeled “achievement” in Fig. 1) but no
effect on the explicit knowledge scores. Moreover, the
difference between ratings for relevant and irrelevant predictors only differed from zero for one of three prediction
tasks. This pattern of results led Evans et al. to conclude:
“we have compelling evidence that performance … was
largely mediated by implicit learning” (p. 615).
There are, however, reasons to question such a strong
conclusion. Participants were faced with different job
tasks in each experimental session, each one involving a
different relevant/irrelevant cue ratio and different
numbers of positive and negative predictors. Self-ratings
of cue relevance were made at the end of each task,
thereby failing the immediacy criterion for assessment
(see Table 1). The sensitivity of the measures can also be
questioned: There were 40 intervening test trials without
feedback before ratings were made, and there were three
different tasks per session, all with common labels for
cues (A–F). Both of these factors could have increased
the chance for cross-task confusion, making the low levels
of explicit knowledge rather unsurprising.
In a recent study Rolison et al. (2011) used similar
methods to investigate the role of working memory capacity
(WMC) in multicue judgment. They found that WMC correlated with performance when tasks involved negative
predictors, but not when all relevant cues were positive
predictors. Rolison et al. interpreted this pattern as evidence for reliance on deliberative processes in tasks with
negative cues, and on implicit processes in tasks with exclusively positive cues. However, their data also showed the
same associations and lack of associations between WMC
and explicit knowledge of the underlying task structure.
Thus a plausible alternative explanation is that performance
was mediated by explicit knowledge in all tasks, but that the
latter sometimes is and sometimes is not related to WMC.
Taken together, these illustrative experiments provide
little evidence that unconscious processes influence
6
multicue judgment. The dominant pattern across the
experiments in both the Evans et al. (2003) and Rolison
et al. (2011) studies was of significant positive correlations
between measures of performance and explicit knowledge
of cue relevance/usage. In those instances where such correlations were absent, procedural artifacts (e.g., timing of
awareness assessment) may have been responsible.
In recognition of the problems of retrospective interrogation of explicit knowledge, Lagnado et al. (2006) used an
approach in which participants learning a multiple-cue
judgment task were probed throughout training trials for
the explicit basis of each prediction. On each trial participants were asked to rate how much they had relied on
each cue in making their prediction. The “explicit” cue
ratings were then compared with the “implicit” weights
derived from running “rolling” regressions (a series of
regressions from predictions to cues across a moving
window of consecutive trials; cf. Kelley & Friedman 2002).
The take-home message from the analysis of these data
was that participants clearly distinguished between strong
and weak predictors on both the implicit and explicit
measures of cue reliance. This ability occurred fairly early
in the task and was maintained or increased across training.
Lagnado et al. (2006) also reported strong positive correlations between individuals’ cue reliance ratings and
implicit regression weights. The overall pattern strongly
suggested that people had access to the internal states
underlying their behavior and that this access drove both
online predictions and explicit reliance ratings. Note that
it is unlikely that the requirement to make online ratings
altered participants’ judgment strategies, as an additional
experiment demonstrated that overall accuracy in the task
was unaffected by the inclusion of the online ratings. In a
recent study, Speekenbrink and Shanks (2010) extended
this approach by using a “dynamic lens model” to assess
participants’ insight in an environment in which cue validities changed across the course of an experiment. Consistent with Lagnado et al. (2006), Speekenbrink and Shanks
found little evidence for the contribution of implicit processes: Participants learned to adapt to changes in the
environment, and their reports of how they changed their
reliance on cues reflected their actual reliance on those
cues as evidenced by their predictions.
2.2 Assessing expert knowledge
Much of the work examining expert judgment has focused
on the necessary antecedent conditions for the development of “intuitive” expertise (e.g., Hogarth 2001; Kahneman & Klein 2009; Shanteau 1992) and the relative
accuracy of expert and statistical judgment (Dawes et al.
1989; Meehl 1954; Vrieze & Grove 2009). Our focus here
is somewhat different; we are interested in the rather
smaller literature that has examined the extent and
nature of experts’ self-insight into the cues they use in
real-world judgment tasks.
Slovic and Lichtenstein (1971) were early to note that
there were “serious discrepancies” (p. 49) between the
explicit weights provided post hoc by judges and the
implicit weights they placed on cues as evidenced by
regression modeling. One source of this discrepancy was
judges’ tendency to overestimate the importance placed
on minor cues and to underestimate their reliance on
major cues. For example, Slovic et al. (1972) reported a
correlation of only 0.34 between the implicit and explicit
weights of 13 professional stockbrokers performing a
stock selection task. The low correlation was attributed to
the variance of explicit weights across the individuals:
Each of the eight predictor variables was rated as most
important by at least one judge, and some variables were
rated subjectively more important than the regression
analysis warranted.
The “serious discrepancies” identified by Slovic et al.
(1972) and many others (e.g., Balzer et al. 1983; Phelps
& Shanteau 1978) seem problematic for the view that we
have access to the information influencing our behavior.
These results would seem to suggest that there are
indeed unconscious influences on the process of weighting
and integrating cue information (see Fig. 1, Point D).
However, the strength with which such conclusions can
be drawn depends crucially on the methods used to elicit
the importance ratings. It is quite possible that judges
have good insight, but that experimenters have not provided them with sufficient opportunities to report the
knowledge that they possess. It is also possible that
judges confuse questions about the “importance” of cues
for the task environment (i.e., ecological validities; see
Fig. 1, Point B) with their “importance” for their own judgment process (i.e., cue utilizations; see Fig. 1, Point D) (cf.
Lagnado et al. 2006; Speekenbrink & Shanks 2010; Surber
1985). As we shall see, there is considerable justification for
these concerns.
2.3 Insight through policy recognition
In an influential brace of articles, Reilly and Doherty (1989;
1992) examined an alternative way of assessing insight and
drew significantly more optimistic conclusions about
experts’ knowledge of their judgment policies. Their
novel procedure used a policy selection or “recognition”
test that involved identifying one’s own policy (described
by normalized cue utilization indices) from an array of possible policies. In both articles, across a variety of hypothetical judgment tasks, this policy recognition method of
assessing insight revealed much higher levels of selfinsight into implicit and explicit policy profiles than indicated in previous research.
Harries et al. (2000) extended the policy recognition
approach by assessing self-insight in medical general practitioners. The doctors had taken part in a policy-capturing
study 10 months prior to the insight assessment. They
had been asked to make prescription decisions (e.g.,
whether to prescribe lipid-lowering drugs) for 130 hypothetical patients, each described by 13 cues (e.g., hypertension, cholesterol level, age), and to rate the importance of
each cue for their judgments. In the follow-up, the
doctors were presented with two arrays each containing
12 bar charts. The first array displayed implicit policy profiles (regression weights), and the second explicit profiles
(importance ratings) both on standard bar charts. The 12
charts included the participant’s own policy and 11 others
randomly selected from the total pool of 32 participants.
Their task was to rank the three policies in each set that
they thought were closest to their own.
Consistent with Reilly and Doherty (1989; 1992), the
doctors were significantly above chance at picking both
types of policies. The average hit rate (having one’s own
policy in the three selected) was 0.48 for implicit and
0.50 for explicit policy recognition. This level of performance is clearly far from perfect but it is considerably
better than the 0.25 hit rate expected by chance. This replication is important because it not only demonstrates
self-insight in genuine domain experts (instead of undergraduate students), but also rules out one possible explanation for Reilly and Doherty’s findings. In their study
some participants mentioned selecting explicit policies on
the basis of explicit memory for the particular numbers of
points they had distributed to individual cues (e.g., “I
know I used 2.5 for one attribute”). Such memory for specifics, rather than insight into the actual policy, is less likely
to have been a contributing factor in the Harries et al.
(2000) study, given that policy recognition was conducted
10 months after the judgment task and importance
ratings were represented as bar charts. Note that although
the test used in these studies does not meet the immediacy
criterion for awareness assessment (see Table 1), the use of
recognition rather than free recall makes it a more sensitive
and arguably relevant test of insight.
The recognition measures used in the Reilly and
Doherty studies revealed an “astonishing degree of
insight” (Reilly & Doherty 1989, p. 125), but the standard
measures (e.g., correlations between implicit and explicit
policy weights) showed the same poor to moderate levels
as seen in many previous experiments. Furthermore, in
both studies predictions on hold-out samples of judgments
(i.e., cross-validation) demonstrated that models using
implicit weights were superior to those using explicit
weights in almost 100% of cases. Thus there appears to
be “something else” captured in the implicit policies that
participants are unable to communicate in their explicit
policies.
However, the lower predictive accuracy of explicit
weights and the tendency for people to state that they
have relied on more cues than are apparent from their
judgments (e.g., Slovic et al. 1972) might also be partially
artifactual. Harries et al. (2000) pointed out that explicit
weight estimates are based on a sample size of one – that
is, they are made once, at the end of a series of (often)
hundreds of judgments. As such they fail the immediacy,
sensitivity, and reliability criteria for awareness outlined
in Table 1. In contrast, the implicit weights are calculated
from all trials and are thus more likely to capture patterns
of cue use. Thus the low correlation between the two types
may be due to the weakness of the cue importance
measure.
The mismatch between stated and actual cue use could
also be attributable to another aspect of typical experimental designs: the use of orthogonal cue sets (cf., Harries et al.
2000; Reilly & Doherty 1992). Policy-capturing studies aim
to discover reliance on particular cues; this is very difficult
to do if a stimulus set contains highly intercorrelated cues,
and so experimenters take pains to develop orthogonal cue
sets. However, this can lead to problems if a judge uses
cues inconsistently across cases.
Harries et al. (2000) cited the example of a doctor using
overweight or blood pressure interchangeably in making a
decision about hypertension (because the two cues are
highly correlated in reality). If the doctor was then presented with hypothetical cases in which these cues were
orthogonal, he or she might still switch between them in
his or her judgments but rate them both highly important
at the end of the task. The regression analysis would then
7
reveal equal but only moderate reliance on the cues, which
would mismatch with the high importance ratings. In
support of this possibility, Reilly and Doherty (1992)
reported higher correlations between explicit and implicit
weights in the representative conditions of their experiments (in which existing cue intercorrelations were maintained) than in their orthogonal conditions (in which they
were reduced/eliminated; see Dhami et al. [2004] for
further discussion of the important impact of representative designs and Beckstead [2007] for an illuminating treatment of the statistical methods for assessing policy
recognition tests).
2.4 Summary and conclusions
The multiple-cue judgment literature presents a rich
source of information about the potential role of unconscious influences. Although the received wisdom in
studies of both novice and expert judges suggests poor
insight into the factors underlying judgment, a close analysis of the data reveals a somewhat more optimistic picture.
Our critique also highlights the importance of distinguishing genuine self-insight (or lack thereof) from artifacts
that are inherent in the methods used to assess judgment.
One possible solution to this problem is to adopt a verbal
policy-capturing method in which structured interviews are
used to elicit explicit policies. Ikomi and Guion (2000) used
such a technique with flight instructors and found that their
“declared” policies were more accurate in predicting judgments than implicit weights for 12 of their 19 participants.
An alternative approach is to reconsider the model underlying judgment. Policy-capturing studies are wedded to the
idea that judgments involve the weighting and adding of
individual cues (i.e., a linear additive model), but people
might be using similarity to previously encountered
instances (Brooks et al. 1991), or applying sequential heuristics (Gigerenzer 2007) in making their judgments. These
judgments might well be consciously mediated but would
appear unconscious if participants were asked to explain
what they were doing in terms of attribute weights, yielding
inadvertent failure to meet the relevance criterion.
More research using various ways of assessing explicit
knowledge is required before strong conclusions can be
drawn, but at the very least we can say that many studies
have revealed reliable access by participants into the
thoughts underlying their judgments.
3. Deliberation without attention: Does “not
thinking” release the powers of the unconscious?
Dijksterhuis et al. (2006b) made the bold claim that when
faced with complex decisions (what car to buy, where to
live), we are better advised to stop thinking and let our
unconscious decide. Dijksterhuis et al. argued that explicit
consideration of options and attributes overwhelms our
capacity-limited conscious thought. In contrast, the unconscious is capacity-unlimited and can therefore weight information appropriately and decide optimally (Dijksterhuis &
Nordgren 2006). In terms of our framework, as with
the studies reviewed in section 2, unconscious processes
are purported to exert influence at Point D in Fig. 1 – the
weighting and integration of information to determine
cue utilizations. Such advice flies in the face of standard
8
prescriptions for decision making (e.g., Edwards & Fasolo
2001; Newell et al. 2007b) and also runs counter to
research that has strongly challenged the related notion
of “incubation” in creative thinking (Weisberg 2006), and
so the evidence on which such claims are based deserves
intense scrutiny.
In the standard experimental paradigm, participants are
presented with information about three or four objects
(e.g., cars) described by 10 or more attributes (e.g.,
mileage) and are asked to choose the best object. In most
experiments best is determined normatively by the experimenter assigning different numbers of positive and negative attributes to each option. Attribute information is
presented sequentially and typically in random order
about the four options. Following presentation of the attributes, participants are assigned to one of three (or sometimes only two) conditions. In the unconscious thought
condition, participants are prevented from making a
decision for a few minutes by engaging in some distracting
activity (e.g., solving anagrams). This distraction period is
claimed to facilitate unconscious thought – “cognitive and/
or affective task-relevant processes [which] take place
outside of conscious awareness” (Dijksterhuis 2004,
p. 586). In the conscious thought condition participants
are asked to think carefully about their choice for a few
minutes, while in the immediate condition participants
are simply asked to make their decision as soon as the presentation phase has finished.
The final choices made by participants in these three
conditions reveal (sometimes) that distraction leads to
better choices and/or better differentiation between good
and bad options than either conscious thought or an
immediate decision. For example, Dijksterhuis et al.
(2006b) reported that 60% of participants chose the best
car after being distracted compared to only 25% following
conscious deliberation. The literature on unconscious
thought is now burgeoning; we focus on two key issues:
the reliability of the effect and alternative explanations
that do not necessitate the involvement of unconscious
processes.
3.1 Reliability of the unconscious-thought effect
Demonstration of the benefit of unconscious thought on
choice requires two criteria to be satisfied. First, choices
following distraction need to be significantly better than
those following deliberation, and, second, they need to be
better than those following an immediate decision. In
view of the amount that has been written about the
merits of unconscious thought, it is surprising how rarely
these criteria have been satisfied in one experiment. Both
criteria are important. Demonstrating that distraction
leads to better choices than deliberation could either
mean that distraction is beneficial or that deliberation is
detrimental. The latter conclusion is less surprising,
especially if the conditions for deliberation are suboptimal
(cf. Mamede et al. 2010; Newell et al. 2009; Payne et al.
2008; Shanks 2006; Wilson & Schooler 1991). The
second criterion is thus a crucial prerequisite for drawing
any conclusions about the added benefit of unconscious
thought.
In the first published work on unconscious thought,
Dijksterhuis (2004) reported three experiments that compared attitude ratings and/or choices following distraction,
deliberation, and immediate processing. None of these
experiments satisfied the two criteria outlined above.
Moreover there were troubling (and unexplained) patterns
in the data. For example, in Experiments 1 and 3 significant
differences between some conditions were only found for
males who constituted the clear minority in the sample.
Thus even in this foundational study the evidence for
unconscious influences was rather flimsy. It appears that
when it comes to the role of unconscious processes, once
an (intuitive) idea has taken hold, a momentum appears
to build that is belied by the strength of the existing data.
But despite this rocky start, it is now clear that there are
several demonstrations of the effect – both in terms of
improvements relative to conscious thought and immediate
thought (see Strick et al. 2011, for a meta-analysis),
although experiments in which all three conditions are
tested and significant differences are found between each
are still the exception rather than the rule (e.g., Dijksterhuis et al. [2009] and Lerouge [2009] – but see GonzálezVallejo & Phillips [2010] for a re-evaluation of the former).
These positive findings are, however, tempered by
several studies that have compared all three thought conditions in a single experiment and failed to demonstrate
any advantage of unconscious thought over conscious
and/or immediate decisions (Acker 2008; Calvillo & Penaloza 2009; Huizenga et al. 2012; Mamede et al. 2010;
Newell et al. 2009; Payne et al. 2008; Rey et al. 2009; Thorsteinson & Withrow 2009; Waroquier et al. 2010). The
reliability of the effect is also questioned by an earlier
meta-analysis of the unconscious-thought literature.
Acker (2008) found that across 17 data sets there was
“little evidence” (p. 292) for an advantage of unconscious
thought. He also found that the largest unconscious
thought effects were in the studies with the smallest
sample sizes. Note that this is exactly the pattern predicted
if one adopts exploratory rather than confirmatory research
practices (Simmons et al. 2011; Wagenmakers et al. 2011)
and is also consistent with a publication bias operating
(i.e., preferential publication of statistically significant
effects – Renkewitz et al. 2011).5 In line with these conclusions, Newell and Rakow (2011) presented a Bayesian
analysis of 16 unconscious-thought experiments from
their laboratories (including both published and unpublished studies) and found overwhelming evidence in
support of the null hypothesis of no difference between
conscious and unconscious thought.
A charitable interpretation is that it is too early to draw
strong conclusions about the robustness of the effect (cf.
Hogarth 2010). Vagaries of procedures, experimental
instructions, differences in population samples, and differences in stimulus materials are all likely to contribute noise
and hamper interpretation. But what about those cases
where an effect is found? Do such results necessitate the
involvement of an intelligent unconscious?
3.2 Explanations of the deliberation-without-attention
effect
Proponents of the unconscious-thought theory (UTT)
argue that deliberation without attention works because
of the increased capacity and superior information-weighting ability of unconscious relative to conscious thought
(Dijksterhuis & Nordgren 2006). However, substantiating
these claims has proved somewhat problematic on both
theoretical and empirical grounds (for a wide-ranging
critique of the “capacity principle” of UTT, see, e.g.,
González-Vallejo et al. 2008). With regard to superior
weighting of information, the experimental evidence is
equivocal at best. In the standard paradigm described
above, participants’ own subjective attribute weightings
are ignored because the importance of attributes is predefined by the experimenter (e.g., Nordgren et al. 2011).
Often this is done in an implausible manner. For example,
in Dijksterhuis et al.’s (2006b) study the number of cup
holders in a car was deemed as important as the fuel
economy (obviously cup holders are far more important):
Both were given the same single-unit weight in the calculation of the best and worst cars. With these experimenter-defined weighting schemes, it is impossible to
know whether the best choice is indeed the one favored
by all participants.
Newell et al. (2009) examined this issue by asking participants, after choices had been made, for importance ratings
for each attribute (e.g., How important are cup holders?).
In so doing, Newell et al. were able to determine, retrospectively, the best option for each participant and then
see how often participants chose the option predicted by
their idiosyncratic weights. The results were clear: Regardless of the condition (conscious, unconscious, or immediate), the majority of participants chose the option
predicted by their own idiosyncratic weights. In a similar
vein, Dijksterhuis (2004) reported that conscious and
unconscious thinkers did not differ significantly in terms
of the correlations between their idiosyncratic attribute
weightings and attitudes toward options.
This last finding was echoed in a recent study by Bos
et al. (2011), who demonstrated that participants in both
an immediate and an unconscious-thought condition were
able to differentiate between cars that had a high number
of “important” positive attributes (quality cars) from
those that had several “unimportant” positive attributes
( frequency cars) (a conscious thought condition was not
included). While unconscious thinkers were significantly
better at this differentiation (their difference scores were
larger), there was no significant difference in the extent
to which participants obeyed their own weighting
schemes. Moreover, because a conscious thought comparison group was not run, we do not know if it was the operation of some active unconscious process that improved
weighting or simply the additional time between presentation of the alternatives and the elicitation of the decision.
A study by Usher et al. (2011) sheds further light on the
weighting issue. They asked participants to rate the set of
attributes from which the objects were composed before
the decision task. A unique set of objects was then
created, via computer software, to ensure that one object
was the best for each individual participant, one the
worst, and two others in-between. The standard decision
task was then conducted with conscious- and unconscious-thought groups (no immediate group was included).
Almost 70% of the distracted participants chose the best
option, while fewer than 30% of those asked to think carefully did so. This is a compelling result suggesting more
optimal weighting in unconscious than conscious thought,
but without the immediate group for comparison, the
Usher et al. results (on their own) do not satisfy our
earlier criteria: The added value of unconscious processing,
relative to an immediate judgment, cannot be assessed.
9
Several authors have asked whether the deliberationwithout-attention effect is due to disadvantages conferred
on conscious thought via particular experimental procedures rather than any hypothesized advantages of unconscious thought. For example, Payne et al. (2008) examined
whether conscious thinkers did poorly in the standard
experimental task because they were forced to think
about the problem for too long. Such persistence could,
according to Payne et al., lead to a shift in attention
toward less relevant information (cf., Rey et al. 2009;
Wilson & Schooler 1991). To test this idea, Payne et al.
compared participants in the standard conscious- and
unconscious-thought conditions with a “self-paced” conscious thought condition in which participants were told
they would have as much time as they liked to deliberate
and decide.” The results were clear-cut: Participants in
the unconscious and self-paced conditions outperformed
those in the conscious condition but did not differ from
each other. Payne et al. interpreted this combination of
findings as evidence for poor performance of inappropriately constrained conscious thought rather than for superiority of unconscious thought.
A second re-interpretation of the unconscious-thought
effect focuses on the possibility that participants make
their decisions before entering the deliberation or distraction periods. The notion is that because attribute information is presented serially (and often randomly) about
each option, participants engage in on-line processing,
updating their impression of each option as subsequent
pieces of information are presented (e.g., Lassiter et al.
2009; Newell et al. 2009; cf. Hastie & Park 1986). In the
distraction condition, where post-information-acquisition
processing is prevented (or discouraged), participants
default to these on-line impressions when asked to make
their final decision. In contrast, those given the opportunity
to deliberate attempt to integrate the large amount of attribute information into a single “memory-based judgment”
(Hastie & Park 1986; Lassiter et al. 2009). The result is
that the retrieved on-line judgments (or first impressions)
are sometimes superior because conscious thinkers are
hampered by fragmentary and poorly organized memory
for the attributes (cf. Shanks 2006). Even authors who
have challenged this interpretation (e.g., Strick et al.
2010) reported that 60% of their participants made
decisions on-line. If this proportion is representative,
then it provides a serious challenge to many previous
studies that have argued that participants deliberate
(either consciously or unconsciously) after information
has been presented (for similar arguments, see also
Newell & Rakow 2011).
Usher et al. (2011) attempted to counter these problems
by using a novel procedure in which multiple periods of distraction/deliberation were interpolated between the presentations of attribute information. They argued that this
interpolation reduced the likelihood of participants deciding before being exposed to the thought manipulation.
Under these conditions a small advantage for unconscious
thought was still found. This result is particularly striking
because the conditions for deliberative thinking were
more suitable – there was less chance that attribute information could have been forgotten, and there were fewer
pieces of information to think about at each “thinking interval.” Why filling these intervals with distraction (anagram
solving) led to improvements in judgment remains a
10
challenge to both the made-the-decision-before and the
poor-conditions-for-deliberation alternative interpretations. However, even Usher et al. did not take this result
as unequivocal evidence for active unconscious processes
(p. 10).
The notion that “sleeping on it,” in the sense of allowing a
passage of time to elapse during which one is distracted,
improves our decisions is enduring, appealing, and in line
with anecdotal experience. Dijksterhuis and colleagues
have struck a chord in the research community (and the
public imagination) with an experimental paradigm that
appears, to some extent, to provide empirical evidence
for the soundness of the deliberation-without-attention
recommendation. What is very clear, however, from our
review is that the robustness and explanation of the deliberation-without-attention effect is far from settled (cf.
Hogarth 2010). Given this state of affairs, suggestions to
rely on the unconscious in applied domains such as legal
reasoning (Ham et al. 2009) seem extremely premature.
One noteworthy feature of the vast majority of unconscious-thought research on decision making is that it has
been done with students making inconsequential, hypothetical choices about situations that they may not have much
experience with – for example, buying cars. Indeed, one of
the few studies that examined the influence of distraction
and deliberation in experts drew rather sobering conclusions for proponents of UTT. Mamede et al. (2010)
showed that expert doctors given a structured diagnosiselicitation-tool during the deliberation period produced
more accurate diagnoses in complex cases than when
they were distracted or made an immediate diagnosis. In
fact, conscious deliberation gave rise to a 50% gain in diagnostic accuracy over an immediate diagnosis. This result
illustrates that experts given appropriate conditions for
deliberation can access relevant knowledge and improve
their reasoning. Interestingly, in the same study novice
doctors made poorer diagnoses in complex cases following
deliberation compared to an immediate judgment (the
accuracy of deliberative and distracted diagnoses did not
differ) – suggesting that the period of structured deliberation is only useful if particular key facts are already part
of one’s knowledge base (Mamede et al. 2010).
In summary, although the deliberation-without-attention
effect has spurred welcome debate, ultimately, even if the
effect can be reliably obtained, its existence falls well short
of providing unequivocal evidence for the involvement of
active unconscious processes in the construction of cue utilizations (Fig. 1, Point D).
4. Awareness in decisions under uncertainty
In decisions under uncertainty, the payoffs from the choice
alternatives are unknown. Repeated sampling can allow
these payoffs to be learned. Decision strategies then translate the learned payoffs into sequences of choices.
4.1 The Iowa Gambling Task
Consider the choice between decks of cards where each
card turned from each deck yields some reward or
penalty, but nothing is known at the outset about the distribution of these outcomes. Someone playing this game has
the opportunity to learn that the long-run payoffs of the
decks differ and hence can adapt their sampling of
the decks to reflect the payoffs. This essential structure
describes the Iowa Gambling Task (IGT), devised by
Bechara et al. (1994). In the years since it was first
described and studied, a vast literature has grown up
around this simple choice task (see Dunn et al. 2006).
The conventional structure of the task employs four card
decks and 100 card selections. Two of the decks yield positive payoffs of $100 for each card, and the remaining two
decks yield payoffs of $50. However, some of the cards
yield simultaneous losses. These are programmed to be
more substantial on the decks that yield $100 payoffs
such that in the long run these decks are disadvantageous
and yield average net losses (equal to −$25), while the
decks with $50 payoffs are advantageous and yield positive
average net payoffs (equal to +$25). Within each pair of
decks, one has larger but less frequent punishments, but
the average payoff is equal. Thus in the long run the best
strategy is to select cards from one or both of the advantageous decks and avoid the disadvantageous ones.
In addition to assessing choice behavior in this task,
Bechara et al. (1997) probed participants’ awareness of
the task structure. After the first 20 trials and then after
every additional 10 trials, participants were asked to
describe what they knew and felt about the task. The
majority of participants eventually reached a “conceptual”
period in which they were able to describe with confidence
which were the good and bad decks, and in this period they
unsurprisingly selected from the good decks on the
majority of trials. Prior to the conceptual period was a
“hunch” period, described by Bechara et al. (1997) as involving a reported liking for the good over the bad decks, but
with low confidence and reports of guessing. In the phase
before this (the “prehunch” phase) participants “professed
no notion of what was happening in the game” (Bechara
et al. 1997, p. 1294). Crucially, then, the question is
whether awareness correlated with card selections or
whether, in contrast, participants selected from the good
decks in the prehunch phase before being aware of the
differences between the decks in terms of their average
payoff. It is this latter outcome that Bechara et al. (1997)
claimed to observe in their data, concluding that “normals
began to choose advantageously before they realized
which strategy worked best” and that “in normal individuals, nonconscious biases guide behavior before conscious
knowledge does” (p. 1293). Elsewhere, it has been
claimed that “this biasing effect occurs even before the
subject becomes aware of the goodness or badness of the
choice s/he is about to make” (Bechara et al. 2000, p. 301).
Studies employing the IGT have a very natural
interpretation within the lens model framework of
Fig. 1. The decks can be conceived of as the cues, and
their relationships to reward and punishment (the criterion) are captured by their ecological validities. The participant’s goal is to judge the likely payoff for choosing
each deck and to make a decision accordingly. If participants indeed learn to make advantageous deck selections,
then their utilizations are appropriately tuned to the validities, yielding high achievement. Inability to report
which are the good or bad decks is unawareness located
at Point B in Fig. 1.
In view of the enormous amount written about the IGT
and this pioneering study, it is remarkable to note that the
key behavioral observation with regard to normal participants – more selections from good than bad decks in the
prehunch period – was not in fact statistically significant
in the Bechara et al. (1997) study. Preference for cards
from the good decks was significant in the hunch and conceptual periods, but by that stage, of course, the participants possessed some conscious knowledge that could be
guiding their choices. And the failure of this preference
for the good decks in the prehunch period to reach significance is unlikely to be due simply to low power, because in
two direct replications, with the same assessment of awareness, Maia and McClelland (2004) and Wagar and Dixon
(2006) did not even observe a numerical preference for
the good decks in the prehunch period.
In addition to their replication of the original study, Maia
and McClelland (2004) tested another group of participants
but employed a much more careful assessment of their
awareness of the nature of the task at regular intervals.
This careful assessment satisfied the criteria listed in
Table 1. Rather than simply recording responses to openended questions regarding what they thought and felt
about the task, Maia and McClelland required their participants to rate each deck on a numerical scale, to explain
their numerical ratings, to report in detail what they
thought the average net winnings or losses would be if 10
cards were selected from each deck, and to state which
deck they would choose if they could only select from
one deck for the remainder of the game. Answers to
these questions provided a range of assessments of awareness against which actual card selections could be compared. In addition, Maia and McClelland ensured that
the classification of decks as good or bad was based on
the actual payoffs experienced by the individual participant
to that point. Bechara et al. (1997) fixed the sequence of
payoffs from each deck in the same way for each participant
and scheduled very few penalties on the bad decks across
the early trials. Thus a participant selecting early on from
the bad decks might actually be making good choices,
because the penalties that ultimately make such decks
bad have not yet been experienced. Plainly, it is crucial to
classify selections as good or bad in relation to what the participant has actually experienced, not in relation to the longterm but unknown average.
When card selections were compared with reported
awareness under Maia and McClelland’s (2004) improved
method, it was apparent that awareness if anything was
more finely tuned to the payoffs than the overt selections
were. Far from observing selections from the good decks
in participants who could not report which were the good
decks, Maia and McClelland found that conscious reports
about the decks were more reliable than overt behavior.
This might indicate that participants were still exploring
the task and acquiring further information about the
decks, but it clearly provides no support for the claim
that nonconscious biases occur before individuals have relevant conscious knowledge. Maia and McClelland’s results
were replicated by Wagar and Dixon (2006), and similar
outcomes were obtained by Evans et al. (2005), Bowman
et al. (2005), and Cella et al. (2007), who in three separate
experiments found that preferential awareness ratings for
the good over the bad decks emerged before the point
at which preferential card selections favored the good
11
decks. By the time behavioral choice revealed a preference
for the good decks, awareness was sharply discriminating.
Maia and McClelland’s (2004) study provides a particularly striking illustration of the dangers of employing an
unreliable or insensitive test of awareness. In the Bechara
et al. (1997) study, normal participants were reported to
progress from the prehunch (no relevant awareness for discriminating the good and bad decks) to the hunch (some
awareness that two of the decks were better than the
others) phases at trial 50 on average, with no participant
making this transition prior to trial 30. In their replication
using the Bechara et al. (1997) awareness questions, but
with a more careful algorithm for making the awareness
classification, Maia and McClelland located the average
transition at about the same point. Yet in their second
group of participants, in whom awareness was measured
via numerical judgments, participants were clearly aware
of the difference between the good and bad decks by the
first assessment at trial 20, and the onset of awareness
has been located at a similar point in other studies (Evans
et al. 2005; Wagar & Dixon 2006). At this point, for
example, 80% of Maia and McClelland’s participants gave
a good deck their highest numerical rating, and 85% of participants indicated one of the good decks when asked which
deck they would choose if they could only select from one
deck for the rest of the game. Thus on the Maia and
McClelland assessment method, most participants had discriminative awareness by trial 20 (and possibly before
then), whereas on the Bechara et al. (1997) method,
none had such awareness prior to trial 30. The openended questions Bechara et al. (1997) used (“tell me all
you know about what is going on in this game” and “tell
me how you feel about this game”), together with their
classification procedure for participants’ responses to
these questions, clearly did not make a sufficiently reliable
and/or sensitive instrument for measuring awareness.
As noted earlier in this article, there has been much discussion about how best to measure awareness. Although
they have attracted considerable controversy, alternatives
to verbal report have been explored. Here we describe
data from one study of decision making in the IGT that
attempted to assess awareness without recourse to reports.
Persaud et al. (2007) required their participants not only
to make a deck selection on each trial, but also to wager on
the payoff for that trial. The wager could either be a high
(£20) or a low (£10) amount. The reward from the advantageous decks was equal to the amount wagered, while
that from the disadvantageous decks was twice the amount
wagered, with occasional penalties being larger on the disadvantageous decks. The point of the task is that wagering is
assumed to provide a probe of the participant’s awareness.
If the participant has some awareness that his or her decision
is a good one, then he or she should be willing to bet higher
on that choice in order to obtain a higher payoff. Thus
Persaud et al. speculated that choices from the good decks
should tend to be accompanied by high wagers and
choices from the bad decks by low wagers, if the participant
has some awareness of the difference between the decks.
In a group of participants tested under these circumstances, the good decks began to be reliably selected by
around trial 40, but wagering did not begin to show a bias
until trial 70. On the basis of this outcome, Persaud et al.
(2007) argued that the initial preference for the good
decks must be based on unconscious information.
12
There are, however, some substantial difficulties with
this set of conclusions. First, to locate the onset of awareness at around trial 70 in the IGT is to run radically
counter to the data obtained in other IGT studies when
the first set of test questions is administered at trial 20.
Several studies (as noted above) have found that the vast
majority of participants give higher numerical estimates
for the good compared to the bad decks the first time
they are questioned (Persaud et al. (2007) did not report
their own results from these awareness questions). Since
the onset of a choice preference for the good decks is
similar in the Persaud et al. study to that found elsewhere
(around trial 40), it seems implausible to argue that the
wagering component made the task harder overall and
therefore delayed the onset of learning and awareness.
Instead, it seems reasonable to speculate that wagering
was measuring something other than awareness, or that it
was measuring awareness insensitively or unreliably. This
latter possibility is consistent with a second problem
facing the wagering method of assessing awareness: Participants may have an aversion to risk or loss and hence may
choose to make low wagers even when they have some
degree of awareness. Evidence that this is not just a theoretical speculation but also an empirical reality has been
reported by Dienes and Seth (2010), and Konstantinidis
and Shanks (2013) have found that when loss aversion is
avoided, wagering very closely matches deck selections.
4.2 Covert emotions in decisions under uncertainty
The review in this section thus far has considered Bechara
et al.’s (1997) behavioral evidence concerning unconscious
biases in decision making. However, that research is influential for a further reason: Physiological markers of
emotion were measured at the same time as card selections. Specifically, Bechara et al. (1996; 1997) measured
their participants’ skin conductance responses (SCRs)
prior to each choice. In normal participants, these
responses, commonly assumed to measure bodily states
of arousal and emotion, were found to be substantial
after both rewards and punishments. Most importantly,
though, they began to emerge during the course of the
task in anticipation of card choices, in particular becoming
larger before selections from bad than from good decks.
Bechara et al. (1996; 1997) took these SCRs to be
“somatic markers,” or covert emotional reactions capable
of influencing behavior unconsciously, suggesting that “a
negative somatic state as hallmarked by an anticipatory
SCR, would nonconsciously “advise” the avoidance of the
disadvantageous decks, while helping bring on line, cognitively the reasons for making the avoidance explicit”
(Bechara et al. 1996, p. 224).
Of course, the evidence described above that participants’ awareness in the IGT is quite extensive raises considerable doubt over the inference that these somatic
markers are in any sense covert. On the contrary, they
may be the effect rather than the cause of conscious
thought, and indeed there is evidence in favor of this viewpoint. Gutbrod et al. (2006) measured SCRs as well as card
choices and found that anticipatory SCRs did not begin to
discriminate between good and bad decks until about trial
80, yet card selections favored the good decks as early as
trial 40. In fact, this sequence is evident in Bechara
et al.’s (1997) data too: Whereas significantly more cards
were selected from good than from bad decks in the hunch
period, anticipatory SCRs measured during that period
were not significantly different for good versus bad decks.
As Gutbrod et al. noted, this early development of a behavioral preference for the good decks cannot have been
driven by the somatic markers measured in anticipatory
SCRs. It could, on the other hand, have been driven by
differential awareness which, as discussed above, emerges
very early in the task. This temporal sequence – awareness
→ differential choice → differential SCRs – seems to fit the
data across these experiments well, with awareness being
evident by around trial 20, advantageous card selections
by trial 40, and differential anticipatory SCRs by around
trial 80.
The only recent study to provide support for the possibility that anticipatory SCRs precede the development of
card selections is that of Wagar and Dixon (2006). These
authors obtained the typical finding of advantageous card
selections emerging at around trial 40, but in their data
differential SCRs were evident by around trial 30. Although
these results suggest that more work is needed before we
fully understand the relative timing of and causal relationship between anticipatory SCRs and card selections, even
Wagar and Dixon themselves did not take any of their
results as evidence of unconscious influences on decision
making. Their participants showed awareness at least as
early as they showed a preference for the good decks.
Moreover, there is a major concern surrounding the
interpretation of somatic markers. On Bechara et al.’s
(1997) interpretation, they provide anticipatory information about the value of a particular choice option,
especially for negative outcomes. Specifically, they are
assumed to encode information about the negative
emotions that were previously triggered by a stimulus or
choice outcome, and then covertly guide subsequent
decisions. On this account, whatever the individual’s
report may state, his or her decision is actually driven at
least in part by an emotional marker of the valence of the
choice outcome, a marker that is related to previous
(especially negative) experiences independently of subjective belief. In contrast to this account, recent findings
suggest that SCRs code the uncertainty associated with
the participant’s decision, not the outcome (Davis et al.
2009; Tomb et al. 2002). For example, Tomb et al.
showed that when the IGT was modified so that it was
the good rather than the bad decks that were associated
with large payoffs and losses, SCRs tended to precede
selections from the good decks. This strongly challenges
the claim of the somatic marker hypothesis that such
markers provide biasing signals for choice, because SCRs
precede those choices (of bad decks) that are eventually
eliminated in the standard IGT and precede those (of
good decks) that eventually dominate in Tomb et al.’s
modified version. Although it is possible that there are
psychologically distinct somatic markers of positive and
negative outcomes, it is plain that they cannot be distinguished by conventional SCR measurement.
Of all the experimental methods used in recent years to
study the role of awareness in decision making, the IGT
and its variants have probably been studied more intensively than any others. The task lends itself quite naturally
to a variety of awareness assessments and a range of behavioral indices, such as card choices and SCRs. While questions remain about important issues such as the suitability
of using wagering as a means of gauging awareness, the evidence (particularly from Maia & McClelland’s [2004] major
study) is clear in showing that participants acquire detailed
conscious knowledge about the payoff structure at an early
point during the task. This awareness emerges at least as
early as behavioral differentiation itself, and there is little
convincing evidence that decision making in the IGT is dissociable from awareness.
5. Primes and primes-to-behavior
In the present section we provide a highly abbreviated
assessment of research using a range of priming techniques
to influence behavior. In some research fields it has
become widely accepted that priming can influence behavior unconsciously.
5.1 Subliminal perception
Subliminal perception is the controversial phenomenon
whereby invisible stimuli may influence some aspect of
behavior (see Fig. 1, Point C). It is intriguing that in the
wake of a comprehensive methodological debate about 25
years ago (see Holender 1986), subliminal processing was
afforded a rather modest role in most theoretical debates
about the causation of behavior. Yet in recent years there
has been a wealth of claims, based on subliminal perception
experiments, concerning the importance of the unconscious
in behavior including some striking reports of subliminal
priming on decision making (e.g., Winkielman et al.
2005). Here we do not attempt to review this extensive literature. We do, however, briefly comment on the pervasive
methodological problems that plague interpretation of
results in this field (Dixon 1971; Holender 1986; Miller
2000; Pratte & Rouder 2009), and we illustrate these problems with reference to a prominent and typical recent
claim about subliminal influences on decision making.
In a striking illustration, Hassin et al. (2007) primed their
participants with a brief (16-ms) masked presentation of
either the Israeli flag or a scrambled version of the flag,
prior to each of several questions about political attitudes
(e.g., “Do you support the formation of a Palestinian
state?”) and voting intentions. Not only did the subliminal
primes influence responses to these questions, but they
also affected subsequent voting decisions in the Israeli
general elections. Key evidence that the primes were invisible came from a test in which participants were shown the
masked images and asked directly to indicate for each
whether it was a flag or scrambled flag, which revealed
chance-level performance.
There are substantial problems with this kind of inference.
For instance, the form of awareness check employed by
Hassin et al. (2007) is susceptible to bias if the participant’s
confidence about seeing the flag is low. On some occasions
on which they actually see the flag, they may nonetheless
respond “scrambled flag” because their judgment is uncertain and they adopt a conservative decision criterion.
Worse still, Pratte and Rouder (2009) have shown that
typical tests used to measure awareness in subliminal perception experiments (such as that used by Hassin et al.
13
2007) may significantly underestimate conscious perception as a result of task difficulty. Because tests assessing
perception of near-threshold stimuli are very difficult, participants may lose motivation. In their experiments, Pratte
and Rouder maintained participants’ motivation by intermixing above-threshold and near-threshold stimuli and
found that identification of the near-threshold stimuli
increased reliably. Thus brief stimulus presentations that
would have been regarded as subliminal in a conventional
awareness test were found to be supraliminal in a modified
test designed to be more closely equated to the main
priming test in terms of difficulty. Until subliminal
priming experiments are able to rule out such artifacts,
their conclusions will remain in doubt. Recent methodological advances (e.g., Rouder et al. 2007) offer the
promise of more clear-cut tests of subliminal perception
in the future.
5.2 Primes-to-behavior
Other striking studies, largely emerging from social cognition, describe apparent influence of primes on behavior
where the prime, but not its influence, is consciously perceived (Fig. 1, Point D). A number of instances have been
reported in recent years, such as that individuals can be
induced to act socially or unsocially, walk faster or slower,
behave more or less intelligently, or perceive accurately or
inaccurately as a result of subtle priming influences of
which they are unaware. In Bargh et al.’s (1996) famous
experiment, for example, participants read sentences containing words related to the concept old age and, as a consequence, a few minutes later walked more slowly down a
corridor. Although few of these studies relate specifically
to decision making, they are provocative illustrations of
possible unconscious influences on behavior.6
Significant question marks exist concerning behavioral
priming studies, particularly in regard to their assessment
of awareness. The methods used for assessing awareness
have generally been weak and fail the criteria described in
Table 1. Bargh et al. (1996), for example, reported an experiment specifically designed to evaluate whether their participants were aware of the potential influence of the prime.
[Participants] were randomly administered either the version of
the task containing words relevant to the elderly stereotype or
the neutral version containing no stereotype-relevant words.
Immediately after completion of the task, participants were
asked to complete a version of the contingency awareness
funnel debriefing ... [which] contained items concerning the
purpose of the study, whether the participant had suspected
that the purpose of the experiment was different from what
the experimenter had explained, whether the words had any
relation to each other, what possible ways the words could
have influenced their behavior, whether the participants could
predict the direction of an influence if the experimenter had
intended one, what the words in the scrambled-sentence task
could have related to (if anything), and if the participant had suspected or had noticed any relation between the scrambled-sentence task and the concept of age. (Bargh et al. 1996, p. 237)
Bargh et al. (1996) reported that only 1 of 19 participants
showed any awareness of a relationship between the stimulus words and the elderly stereotype.
This experiment leaves a number of questions unresolved. For example, was there any difference between
14
the two groups in their responses to any of the questions?
No actual data were reported at all, let alone broken
down by group. Why were questions about whether the
purpose of the experiment might have been different
from what the experimenter had explained, and about
whether the words had any relation to one another,
included in the awareness test? These issues are irrelevant
to the critical issue, namely, whether the participant was
conscious of the activation of the age concept. The only relevant question is the final one, whether the participant had
noticed any relation between the scrambled sentences and
the concept of age. All the other questions are irrelevant,
and their inclusion simply adds noise to the overall score.
Put differently, the groups may have differed on their
answers to this question, but that difference might well
have been submerged in the random variance added by
the other questions. Worse still, Doyen et al. (2012) used
the same walking speed task but with more careful awareness debriefing: Participants were required to choose
among four pictures representing categories that could
have been used as primes (athletic person, Arabic person,
handicapped person, elderly). Doyen et al. found that
primed participants had significantly greater awareness of
the prime on this test than unprimed participants.
Unfortunately, weak methods are still being employed.
In Ackerman et al.’s (2010) recent report that various
social judgments can be nonconsciously influenced by
haptic sensations, the only supporting evidence regarding
awareness was that “Only one participant (in Experiment 5)
reported awareness of the hypotheses, and so this person
was removed from the analyses” (supplementary materials).
How participants were probed about the influence of
the primes on their behavior is not described, and
whether or not they would have reported awareness if
the criteria described in Table 1 had been satisfied
(e.g., using sensitive methods such as rating scales) is
unknown.
Another major problem is that the replicability of many
of these priming effects has yet to be established. Dijksterhuis et al. (1998, study 2), Doyen et al. (2012), and Pashler
et al. (2011) all failed to replicate Bargh et al.’s (1996)
finding that priming the stereotype of elderly people can
affect walking speed. In another priming situation, Bhalla
and Proffitt (1999) reported that participants judged a hill
as steeper when they were wearing a heavy backpack, but
results from Durgin et al. (2009) found evidence that this
priming effect is an artifact of compliance by participants
to the perceived experimental hypothesis. In yet another
example, Zhong and Liljenquist (2006) reported that
asking participants to recall an unethical act from their
past increased the accessibility of cleansing-related words
and the likelihood of taking antiseptic wipes, yet the only
published attempt to replicate these findings yielded four
failures (Gámez et al. 2011). Until clear replications of
these priming effects are reported, using more sophisticated assessments of awareness, it is premature to conclude
that these studies provide robust evidence of unconscious
influences on behavior.
Few topics in psychology excite as much attention in the
media as research on priming effects with subtle but unnoticed or outright subliminal stimuli. Yet research in this
field yields remarkably few effects that convincingly
demonstrate unconscious influences. The claim that
briefly presented primes fall outside consciousness is difficult to establish without extremely rigorous methods, but
such methods are employed insufficiently often. The
primes-to-behavior literature has also largely employed
weak methods to assess awareness, there are question
marks over the replicability of some of its most prominent
findings, and selective publication bias and file drawer
effects (Renkewitz et al. 2011) may be clouding the
overall picture.
6. Discussion
We have articulated some of the conditions necessary to
establish influences of unconscious mental states on
decision making and have reviewed a considerable body
of evidence in relation to multiple-cue judgment, deliberation without attention, decisions under uncertainty, and
priming. From the perspective of our lens model framework, many of the claims for unconscious influences
focus on Points B (unawareness of cue–criterion relations),
C (unawareness of cues), and D (unawareness of cue utilization) (Fig. 1). However, when paradigm demonstrations
are scrutinized, explanations that invoke unconscious processes appear unnecessary. Performance in tasks such as
the IGT which is routinely cited as providing evidence for
unawareness (at Point B) can be readily explained as
mediated by conscious acquisition of deck knowledge
(Maia & McClelland 2004); subliminal priming experiments that might be considered optimal for demonstrating
unawareness at Point C reveal awareness of primes (Pratte
& Rouder 2009); and studies of multiple-cue judgment
suggest that people do possess knowledge of cue utilization
(Point D; Reilly & Doherty 1992). Moreover, manipulations designed to impact this utilization process unconsciously have limited and potentially artifactual effects
(Newell et al. 2009; Payne et al. 2008). In summary,
these research areas have so far failed to yield clear, replicable, and unequivocal demonstrations of unconscious influences. On the contrary, many careful experiments have
documented consistently high levels of conscious access
in people’s causal reports on their behavior.
A surprising outcome of the review is that debates and
disagreements about the meaning of the terms consciousness and awareness have (with a few exceptions) played a
remarkably minor role in recent research. Whereas issues
about how to define and measure awareness were once
highly prominent and controversial (e.g., Campion et al.
1983; Reingold & Merikle 1988), it now seems to be generally accepted that awareness should be operationally
defined as reportable knowledge, and that such knowledge
can only be evaluated by careful and thorough probing.
Thus an encouraging conclusion is that the field seems to
have generally taken heed of detailed recommendations
(e.g., Ericsson & Simon 1980) about suitable methodology
in the assessment of awareness, including the requirements
noted in Table 1 that awareness assessment must be
reliable, relevant, immediate, and sensitive. We concur
with Uhlmann et al. (2008) that claims of unconscious influences should ideally depend on more than simply confirming the null hypothesis (that evidence of awareness is not
obtained). Null results are always ambiguous because the
assessment may not have adequately met the criteria in
Table 1. Uhlmann et al. proposed a range of other findings,
such as the absence of actor–observer differences, which
may avoid these difficulties (though, as previously discussed, these findings have not been obtained under
more careful assessments).
6.1 Brief comments on other research areas
We noted in the Introduction that our focus is on those
research areas that are most relevant to our overall question
about the extent to which the mental processing that leads
to the selection of one among several actions can be considered unconscious. To talk of brain systems making
decisions is to use the notion of a decision very differently,
and it is not clear what it would mean to ask whether the
visual system’s computation of size and distance, for
example, is or is not conscious. Even with regard to the
main areas reviewed in Sections 2–4, we have of necessity
been selective in the studies we have reviewed, and we
recognize that our critical viewpoint leaves us at risk of
the objection that if we had considered areas X or Y, we
would have found more compelling evidence. We maintain
that the areas we have selected have been highly influential
in bolstering claims for unconscious decision making, so it
would be very surprising if the evidence is markedly
weaker in these domains than elsewhere. Nevertheless,
we briefly comment here on some other well-known
areas. Our hope is that we can convey at least a flavor of
why the common claims from these fields may be open to
challenge.
6.1.1 Automaticity. When we look at the individual’s use of
low-level brain “decisions,” do we see clear evidence of
unconscious processing? This is far from obvious. Evidence
that such decisions are cognitively impenetrable (in other
words, immune from top-down attentional control and conscious knowledge) is very controversial. It is now known, for
instance, that even something as low level and apparently
automatic as motion processing in area V5 is dramatically
attenuated in conditions of high cognitive load (Rees
et al. 1997). Similarly, visuo-motor adaptation is influenced
by conscious expectancies (Benson et al. 2011).
Supposedly automatic processes like word reading and
visual adaptation are frequently cited as examples of unconscious processing. The use of unconscious (meaning
uncontrollable influences/processes) in this context is
rather distinct, however, from what we have been concerned with in this article (unreportable influences or processes). The evidence suggests that very few influences or
processes are truly uncontrollable. For example, it is wellknown that Stroop interference – which apparently
reveals the automaticity of word reading – can be diluted
by a range of manipulations of top-down control (Logan
& Zbrodoff 1979). Thus these examples have little
bearing on the main question addressed in the present
article.
6.1.2 Neural precursors of motor responses. Famously,
Libet and colleagues (Libet 1985; Libet et al. 1983)
reported experiments in which electroencephalographic
activity was monitored while participants freely chose
when to make a voluntary movement and reported the
time point at which they felt the intention to move (Point
15
E in Fig. 1). Participants observed a spot rotating on a clock
and made their timing reports by observing the dot’s
location at the point of becoming conscious of their urge
to move (these are called “will” or W judgments). Libet
found that these judgments followed rather than preceded
the first neural marker of movement intention, the readiness potential (RP), and indeed the time interval between
these could be as much as a second. Libet and many subsequent commentators have taken these results as evidence
that conscious intentions do not cause voluntary actions but
are instead epiphenomenal effects of the true, unconscious
causes of such actions, namely, neural events. Recent
research has extended the method using recordings of
activity in single neurons in medial frontal cortex (Fried
et al. 2011), which show progressive recruitment over
several hundred milliseconds prior to participants’ reported
experience of the urge to move.
Several recent studies, adopting variants of Libet’s
method, serve if anything to support the intuitive Cartesian
view that voluntary movements are caused by conscious
decisions to act. Particularly noteworthy is a study by
Trevena and Miller (2002) that compared lateralized readiness potentials (LRPs) to psychometrically determined W
judgments. The LRP is a more appropriate indicator of
hand-specific movement preparation than the RP, which,
Trevena and Miller argued, is a marker of very general
preparation for a future movement. As well as replicating
Libet et al.’s (1983) finding that RPs preceded the mean
time of W judgments, Trevena and Miller (2002) also
found that the same was true for LRPs, although by a
much smaller amount (approximately 180 ms). Crucially,
however, Trevena and Miller pointed out that comparing
the onset of one measure (LRP) with the mean of
another introduces bias. Instead, one onset needs to be
compared with another. In their experiments, Trevena
and Miller were able to determine the earliest point at
which W judgments occurred (i.e., the onset of these judgments) and found evidence that they tended to precede,
not follow, the LRPs.
An additional finding confirms that the RP is not – as
Libet et al. (1983) supposed – an appropriate measure of
preparation for action execution. Miller et al. (2011)
reported the striking finding that the RP “signature” of
movement preparation was virtually eliminated in conditions where participants made voluntary movements
but without a clock or any requirement to report W judgments. The implication of this is that the preparatory
neural activity, which Libet took as evidence of unconscious
movement preparation, has more to do with dividing attention and preparing to make a clock judgment. As Miller
et al. noted, the clock procedure, which was designed to
measure mental events, seems in fact to alter the neural
activity to which these mental events are related.
6.1.3 Conscious will as an illusion. The folk-psychological
view that conscious thoughts cause our decisions and behavior faces a major obstacle in the substantial body of evidence suggesting that our conscious thoughts are often
inferred after the fact. Rather than making conscious
choices and immediately and passively experiencing those
thoughts, an alternative possibility is that the thoughts are
constructions created post hoc and that the true causal
work is done by unconscious states of mind and brain.
This is the essence of the will-as-illusion viewpoint, which
16
emphasizes that experiencing an intention prior to an
action is no guarantee that the intention caused the
action. In one particular version of this approach, and in
contrast to the intuitive view that our decisions and behaviors are caused by conscious intentions, it has been
argued (particularly by Wegner 2004) that they are
instead caused by unconscious processes that may simultaneously produce illusory experiences of conscious will.
Specifically, it is proposed that unconscious states of
mind/brain cause two things, both the voluntary action
itself and a conscious thought about the action (intention).
As a result of the constant conjunction of thought and
action, an experience of will is created via illusory inference
even though the thought itself is not the true cause of the
action. Wegner drew an analogy with a ship’s compass.
Someone looking at the compass and relating it to the
ship’s course might form the impression that the compass
is actually steering the ship, yet we know that the
compass exerts no such control over the ship’s movement.
The compass reading is an effect, not a cause, of the ship’s
course, which is in fact caused by a whole raft of separate
factors and processes such as the prevailing wind and the
position of the ship’s wheel and rudder.
Wegner’s principal support for this theory comes from
demonstrations that illusions of will can be created in
which people either experience will when their conscious
thoughts are objectively not the cause of their actions or
fail to experience will when they objectively are. For
example, Wegner et al. (2004) had participants watch
themselves in a mirror with their arms out of view by
their sides while a confederate stood behind them. The
confederate’s arms were extended forward to where the
participant’s arms would normally be, and these arms performed various actions such as giving an OK sign. When the
participants heard instructions over headphones previewing each of these actions, they judged that they had
greater control over the arms’ movements. Wegner has
concluded from such demonstrations that the experience
of conscious will is an illusion in the same sense that the
experience of physical causation is. In both cases, our
minds draw inferences when the conditions are appropriate, namely, when constant conjunction is present.
There have been numerous responses to Wegner’s
radical position on will and the conscious causation of behavior. Nahmias (2005) pointed out that the experiments
do not induce anything remotely resembling full-scale
experiences of agency. In Wegner et al.’s (2004) study,
for example, participants rated their sense of vicarious
control on 7-point scales (on which 1 = not at all and 7 =
very much). Although participants reported a significantly
enhanced feeling of control when the actions were previewed auditorily, their average ratings were never
greater than 3 on this scale. Hence it can hardly be
claimed that they reported experiencing a feeling of
control over the confederate’s actions. Moreover, it has
been noted (McClure 2011) that we often experience will
even when an intention precedes an action by a long interval (such as a vacation). The analogy with physical causation
is curious because the conclusions drawn in the two cases
seem very different. In the case of physical causation,
even if it is accepted that our knowledge of causation is
an inference based on constant conjunction, and that we
can in consequence experience illusions of causation,
most people do not conclude that physical causation itself
is a fiction or that perception is generally illusory. Rather,
we conclude that there are real causal connections in the
world but that our knowledge of them is indirect and
largely inferential (e.g., Harré & Madden 1975). In contrast, on the basis of illusions of agency and will,
Wegner’s conclusion is that free will and the conscious causation of behavior are illusions. The illusions per se cannot
prove this. They merely show that we lack direct access to
linkages between thought and action.
6.1.4 Blindsight. Individuals with the condition known as
blindsight report being experientially blind in a part of
their visual field (scotoma) yet are able to make a variety
of discriminations about stimuli presented in that part of
the field. Blindsight results from damage to primary
visual cortex, and because external space is represented
retinotopically in primary visual cortex, there is a tight
coupling between the location of the cortical damage and
the location of the scotoma. Successful discrimination of
location, movement, form, color, and so on, as well as
overt actions such as pointing, have been reported in blindsight (Weiskrantz 1986), and it has been proposed that
these behaviors must be based on unconscious representations, as blindsight patients deny visual consciousness
regarding stimuli falling within their scotomata. In terms
of the lens model, the deficit is located at Point C in
Figure 1.
For almost as long as blindsight has been investigated,
the possibility that the condition is simply degraded
(near-threshold) normal vision has been hotly debated
(see Campion et al. 1983; Weiskrantz 2009). It is possible
that residual visual discriminations with near-threshold
stimuli are accompanied by weak, but reliable, levels of
visual awareness. In fact, individuals with blindsight often
report forms of visual experience (Overgaard 2011).
Cowey (2010) recently noted in regard of D.B., the
patient whose performance led to the coining of the term
blindsight, that “there is still no explanation … for the revelation nearly 30 years after his operation, that he experiences visual after-images when a visual stimulus is turned
off.… How ironic if the discovery of blindsight proves to
be based on a patient who does not possess it!” (p. 7).
Weiskrantz (2009) and others have argued against the
degraded normal vision hypothesis by pointing out
(among other things) that individuals with blindsight
behave qualitatively differently from normal individuals.
Signal detection theory can be used to show, for instance,
that forced-choice guessing in blindsight about which interval contained a stimulus yields a higher discrimination
measure than yes/no responses about whether a stimulus
was presented, while in normal individuals measured discrimination is identical in the two conditions (Azzopardi
& Cowey 1997).
Overgaard and colleagues (Overgaard 2011; Overgaard
et al. 2008; Ramsøy & Overgaard 2004) have argued,
however, that dichotomous measures that ask the individual to report (yes/no) whether a stimulus is visible systematically underestimate the extent of visual awareness
(regardless of response bias). These authors have provided
evidence that when participants (both normal and blindsight) are given the opportunity to report the clarity of
their perceptual experience using a range of categories
such as “no experience,” “brief glimpse,” “almost clear
experience,” and “clear experience,” stronger correlations
are observed between awareness and discrimination accuracy than is the case when awareness is measured with
binary responses. As with other examples from neuropsychology, much of the evidence can be plausibly explained
without recourse to unconscious influences.
6.2 The seduction of the unconscious
Given these conclusions, it is surprising (to us) that there
remains a pervasive view in the literature that unconscious
processes serve an important explanatory function in theories of decision making. This prominence is most
obvious in theories that contrast deliberative with intuitive
decision making (see Evans 2008; Kahneman 2011; Keren
& Schul 2009). A recent version of this general view advocates two interacting systems with the following qualities:
System-1 (intuition) is parallel, extracts gist (holistic), and
results in affective states, which are open to phenomenological
awareness (Block 2007) in their end result but not in their operation (or stages). While, in contrast, system-2 (deliberation) is
sequential, rule-based (e.g., lexicographic), and has access to
the stages of processing. (Usher et al. 2011, p. 10, emphasis
added)
Our added emphasis highlights that Usher et al. (2011)
operationalized the two systems, in large part, via access
to phenomenological awareness. In essence, Usher et al.’s
interpretation suggests that a decision maker relying
purely on system-1 would have awareness only at Point E
in Figure 1 (the “end result”), whereas one relying solely
on system-2 would be aware at all points (A–E inclusive).
Usher et al. emphasized, however, that many decisions
will be a product of these two systems interacting. For
example, in a multi-attribute judgment task, system-2 is
responsible for sequentially inspecting attributes and
alternatives (e.g., Does this car have cup holders?), while
system-1 generates an “affective integration of the values”
(p. 10). This approach is similar to that proposed by Glöckner and Betsch (2008) in their parallel-constraint satisfaction model of multi-attribute judgment and choice (see
also Glöckner & Witteman 2010). Our review suggests,
however, that when participants are given adequate opportunities to report the knowledge underlying their behavior,
there is little, if any, explanatory role played by a phenomenologically inaccessible affective integration process.
While knowledge underlying behavior might not always
be comprehensive, it is often sufficient to explain observed
performance.
Why, then, do explanations that invoke unconscious
mental states remain so popular? A superficial answer is
that they make good stories that have clear appeal to a
wide audience, especially when they involve expert decision
making (e.g., Gladwell 2005; Lehrer 2009). A more considered answer acknowledges that as a field of study, the
issue of unconscious influences is a challenging one to
look at impartially because we all have such strong ex
ante beliefs about the causation of our behavior and the circumstances in which we are unaware of its determinants.
Consider the following illustration of the grip that our intuitions about the limits of conscious deliberation can hold. In
a multiple-choice test, is it wise to change your answer on
subsequent reflection? Suppose that you have been asked
which city is more populous, Stockholm or Munich? You
intuitively choose Stockholm, but then ponder your
decision further. Perhaps you retrieve relevant information
17
from memory such as that Munich has a famous soccer
team whereas Stockholm does not. Should you change
your answer in such circumstances where intuition and
reason diverge? A majority of people believe that the
answer is “no,” and students often resist (and are advised
by their teachers to resist) revising their initial responses,
yet decades of research proves the contrary (e.g., Benjamin
et al. 1984). In fact, revising initial answers tends to
increase their accuracy (Munich is in fact more populous).
In the face of such strong but mistaken intuitions about
conscious deliberation, and the likely confirmation biases
they induce, empirical evidence faces an uphill battle.
Of course there are well-developed and influential frameworks that seek to offer principled accounts of the distinction between conscious and unconscious processing.
The well-known global workspace theory of Baars (2002),
for instance, roughly divides conscious from unconscious
processes in terms of events that are or are not in the spotlight of selective attention. But such models start from the
assumption that unconscious drivers of behavior exist, and
this is the very assumption we believe is in need of critical
scrutiny. We argue that many reports on unconscious biases
have been influential in part because the audience has been
strongly predisposed to believe them, even when alternative interpretations are available. Thus, claims about the
role of unconscious processes have not always been
treated quite as critically by the academic community
(including journal editors) as claims for which our intuitions
are weaker. This can lead to the momentum effects we
noted in our review in which (weak) evidence for an “intuitive” result is given undue weight and forms the basis for
largely black-box explanations of behavioral phenomena.
This is not to deny that there are differences (phenomenological and otherwise) between a deliberation-based and
an intuition-based decision. Nor is it to deny that sometimes deliberated decisions can be bad (e.g., Ariely &
Norton 2011; Wilson & Schooler 1991), and fast decisions
can be good (e.g., Goldstein & Gigerenzer 2002).
The first of these claims – that too much thinking about a
decision can lead to poorer choices than only thinking a
little – is not necessarily at odds with our framework. The
deleterious effect of reasons analysis (conscious reporting
of the bases for choice) would be captured by the inclusion
of too many inappropriately weighted cues (Point D,
Fig. 1). Such an effect does not necessitate unconscious
influences but rather the ineffective use of conscious deliberative processes. If choices (or attitudes) change when
people are asked to report underlying reasons, this does
not necessarily imply that initial choices are the product
of unconscious reasons (e.g., Uhlmann et al. 2008). A
choice might change because additional information to
that originally considered consciously might alter (sometimes detrimentally) a final choice. The simple point is
that these decisions need not be based on unconscious
knowledge – in the sense of lack of awareness of the knowledge and inferences underlying one’s behavior.
With regard to the second claim – that fast decisions can
be good – Simon’s succinct statement that intuition is
“nothing more and nothing less than recognition” (Simon
1992, p. 155) is a useful insight here7 (cf. Kahneman &
Klein 2009). Simon’s analogy with recognition reminds us
that intuition can be thought of as the product of overlearned associations between cues in the environment
and our responses. In the same way that firefighters train
18
for many years to recognize cue–outcome associations
(e.g., Klein 1993), we all learn to make a multiplicity of
mundane everyday decisions (what to wear, eat, watch on
TV, etc.). Such decisions may appear subjectively fast and
effortless because they are made on the basis of recognition: The situation provides a cue (e.g., portentous
clouds), the cue gives us access to information stored in
memory (rain is likely), and the information provides an
answer (wear a raincoat) (Simon 1992). When such cues
are not so readily apparent, or information in memory is
either absent or more difficult to access, our decisions
shift to become more deliberative (cf. Hammond 1996;
Hogarth 2010). The two extremes are associated with
different experiences. Whereas deliberative thought
yields awareness of intermediate steps in an inferential
chain, and of effortful combination of information, intuitive
thought lacks awareness of intermediate cognitive steps
(because there aren’t any) and does not feel effortful
(because the cues trigger the response). Intuition is,
however, characterized by feelings of familiarity and
fluency. Again, the simple point is that in neither situation
do we need to posit “magical” unconscious processes producing answers from thin air (cf. Hogarth 2010; Kahneman
& Klein 2009). As we have seen, when one undertakes a
critical examination of the empirical evidence for genuine
unconscious influences on decision making, the evidence
is remarkably weak.
6.3 Recommendations for future research
What recommendations can be drawn from past research
that might fruitfully guide future explorations? One recommendation concerns the types of task that are studied.
It is hard to foresee much progress if the focus is on
highly reflective situations such as the IGT or experiments
in the deliberation-without-attention framework. These
experimental tasks explicitly instruct the participant to
make a particular decision and either seek evidence that
introspective reports about the decision process are incomplete (IGT) or else that promoting further conscious reflection hinders decision accuracy (deliberation-withoutattention studies). Moreover, in these sorts of tasks,
participants are fully aware of the independent variables
(reward magnitudes in the IGT, positive and negative attributes in deliberation-without-attention studies). It seems
unsurprising that when participants are focusing on a particular decision problem, their introspections about their
own mental processes and about the influences of the independent variables on their behavior can be quite insightful.
Turning to neuroscience and neuropsychology for clear
answers also appears problematic given the controversies
surrounding many purported demonstrations of unconscious influences (see sect. 6.1).
More promising is to look at situations in which attention
is diverted away from the experimenter’s hypothesis, such
as those that look for subtle priming influences on behavior.
The prime-to-behavior literature (sect. 5.2) includes many
examples in which participants are unlikely to be aware of
the potential influence of a prime on their behavior.
Although the interpretation and replicability of many of
these findings is in dispute, the basic logic of the experiments seems sound for investigating unconscious influences. A notable example of a very different type is
Richardson et al.’s (2009) demonstration that choices can
Commentary/Newell & Shanks: Unconscious influences on decision making
be influenced by relating the choice to eye movements.
Participants in their study considered questions such as
“Is murder sometimes justifiable?” and their gaze was monitored as they looked at on-screen yes and no buttons. They
were required to choose as soon as the buttons disappeared. Richardson et al. arranged for the buttons to disappear when the participant’s gaze had rested on one of them
for 500 ms and found that “yes” responses were about 10%
more likely when gaze had been on the yes than the no
button. This and many other such subtle priming effects
offer considerable promise for future exploration of
insight, awareness, and decision making.
6.4 Conclusion
In summary, evidence for the existence of robust unconscious influences on decision making and related behaviors
is weak, and many of the key research findings either
demonstrate directly that behavior is under conscious
control or can be plausibly explained without recourse to
unconscious influences. Few topics in the behavioral
sciences are as fundamental as this or run across as many
subdisciplines of experimental psychology. Future research
must take seriously the experimental and theoretical challenges that our critical review has highlighted.
are unable to control the influence of Afrocentric features on
their judgments (Blair et al. 2004b).
5. A more recent and much larger meta-analysis of the unconscious-thought literature by Strick et al. (2011) does not discuss
the relationship between N and effect size.
6. Some of these effects have been interpreted as arising from
direct perception–action links, and if that indeed is their basis,
then the involvement of decision making processes would be
minimal. However, it has become clear that these effects are
highly inferential and almost certainly do recruit aspects of
central decision processes (see Loersch & Payne 2011). For
example, primes sometimes produce assimilative effects and
sometimes contrast effects.
7. In a similar vein, Albert Einstein once noted that “intuition
is nothing but the outcome of earlier intellectual experience” (In a
letter to Dr. H. L. Gordon on March 5, 1949; Albert Einstein
Archives 58–217, as cited in Isaacson 2007.)
Open Peer Commentary
ACKNOWLEDGMENTS
The support of the Australian Research Council (DP
0877510; DP 110100797; FT110100151) to Ben
R. Newell is gratefully acknowledged. We thank Magda
Osman, David Lagnado, Peter Lovibond, Adam Harris,
and Jon Baron for insightful discussions and comments
on an earlier draft of the manuscript. We also thank the
University of New South Wales students who participated
in the 2010 and 2011 Unconscious Influences on Decision
Making seminar course.
NOTES
1. We use the terms decision making and judgment throughout
the article. A decision can be conceptualized as guided by the
judgment process, which in turn refers to the evaluation of evidence pertaining to different options (see Baron 2008).
2. Our illustrative use of the lens model departs somewhat
from traditional Brunswikian perspectives (e.g., Dhami et al.
2004). For example, in our conceptualization the criterion
(Point A) can exert a causal influence on judgment.
3. The terms proximal and distal here refer to temporal
characteristics of the cues and should not be confused with the
use of the same terms in the traditional lens model framework.
4. Because this point is so crucial we provide another illustration of our alternative approach to explaining cases where individuals appear unaware of an influence on their behavior. In
striking research on racial stereotypes in criminal sentencing,
Blair et al. (2004a) found that both black and white prison
inmates with more Afrocentric features (e.g., darker skin, wide
nose, full lips) received harsher sentences than those with fewer
such features, and suggested that this form of stereotyping is
outside people’s awareness and control. But Blair et al. (2004a)
provided no evidence that number of Afrocentric features was
the proximal cause of behavior, and it is easy to imagine that
some other feature was instead. For instance, suppose that
number of Afrocentric features in faces is correlated, in the
minds of judges, with some other attribute such as hostility or
low intelligence. Use of this correlated attribute might be entirely
conscious (though of course deeply unjust). Moreover, it would
not be surprising on this alternative hypothesis that participants
Degraded conditions: Confounds in the study
of decision making
doi:10.1017/S0140525X13000629
Louise Antony
Department of Philosophy, University of Massachusetts, Amherst, MA 010039269.
lantony@philos.umass.edu louise.antony@gmail.com
Abstract: I raise a consideration complementary to those raised in the
target article. Many of the most widely cited studies on decision making
involve introspection in degraded conditions, namely, conditions in
which agents have no reason for the decisions they reach. But the fact
that confabulation occurs in degraded conditions does not impugn the
reliability of introspection in non-degraded conditions, that is, in cases in
which a subject actually does make a choice for a reason.
An ongoing debate in philosophy of mind concerns the status of
our everyday, “folk psychological” explanations of human actions –
explanations that advert to the agents’ intentions or goals. It is
part of this folk picture that in cases where people do act for
some particular reason, they know what that reason is. But the
work surveyed by Newell & Shanks (N&S) suggests that this
assumption is false. Indeed, some of it suggests not only that we
may be frequently wrong about what our reasons are, but also
that we may be wrong about having reasons at all. To be a
reason for performing an action, a mental state must have semantic content, and that content must bear a rational relation to the
agent’s conception of the action to be performed. Thus, a judgment that stocking sample D is superior to the other samples
would rationalize choosing sample D. But if the factor that in
fact determines this choice is the spatial position of sample D,
there is no rationalizing. Even if I were aware of a rightward
bias, the thought that sample D is the rightmost sample would
not give me a reason for choosing sample D. If such a factor
were in play at all, it would likely be as a mere physical cause:
The perceived position of the sample would immediately determine the choice, without any cognitive mediation at all. Unsurprisingly, then, reductivist and eliminativist critics of folk
psychology point to work like that of Nisbett and Wilson to
19
argue that our ordinary attributions of intentions are not only frequently false, but also explanatorily otiose (Churchland 1988).
Some philosophical defenders of folk psychology dismiss such
arguments as irrelevant to the probity of the belief-desire framework: Explanation within such a framework, they say, is different
from causal explanation, and can be justified independently of any
causal account of the production of action (Blackburn 1986;
McDowell 2004). I am, however, staunchly naturalistic in my
approach to the mind, and so am committed to the continuity of
philosophical and empirical work. I therefore recognize the inprinciple relevance of the work in question and the seriousness
of the challenge it poses. I am very glad, therefore, for the critique
N&S offer. I would like to highlight some aspects of their critique
that I find particularly germane to the philosophical debate, and
then complement the critical points made by the authors with
considerations that raise additional questions about the work in
question.
N&S point out that Nisbett and Wilson’s landmark “stocking
study” (Nisbett & Wilson 1977), alluded to earlier, fails to meet
at least one of their criteria of adequacy for assessments of awareness, namely, relevance. Although spatial position was correlated
with subjects’ choices, Nisbett and Wilson illegitimately
presume that spatial position per se was causally relevant to
their subjects’ choices. But this inference neglects the possibility
that subjects were running a left-to-right sequential evaluation
of the stockings and operating with the rule “if the next one is
as good as the previous one, go with the next one.” Indeed,
Nisbett and Wilson themselves report evidence that suggests
that subjects were doing something like this. I find this point
especially significant for philosophy, because it underlines the
importance of taking seriously cognitive states and processes as
independent variables in the production of behavior, variables
that must be studied and controlled for. This is as against the
strict behaviorist model (which seems to have more vitality in philosophy than in psychology), which only considers publicly available factors – observable stimuli and behavioral responses – and
the reductionist/eliminativist model, which says that it is otiose
to posit states at any level of abstraction above the neurophysiological level.
But it’s one thing to say that the proximal causes of the subjects’
choices were cognitive and another to say that they were introspectible. If N&S are correct about the cognitive procedure the
subjects were utilizing, why did the subjects not report that?
Why did they insist that their choices were based on the superior
quality of the stocking they chose? I have a hypothesis: The set-up
of this experiment is a virtual invitation to confabulation. Since
there is no good basis for preferring any one sample to any
other, subjects will, typically, not be able to cite any such basis.
Hence any reason proffered by the subject is going to be
wrong. But what does a subject’s behavior in this sort of circumstance tell us about the accuracy of introspection in cases in
which the subject does have a reason for acting as he or she does?
What I am suggesting is that Nisbett and Wilson were investigating introspective awareness under degraded conditions. In
general, it cannot be assumed that the way we solve problems
in normal conditions is the same as the way we solve them in
degraded conditions. (Consider the very different visual processes
activated in daylight and in low light.) Inferences about the
unreliability of a certain cognitive process in degraded conditions
should not be taken as evidence that the same process is unreliable in normal circumstances. (If we assessed color vision by
looking at its operation in low light, we’d conclude that we are terrible at judging colors.) It could well be, therefore, that introspection is highly reliable when our choices and actions are the result
of reasons – that is, when there are reasons there to be introspected – but that we have to employ other methods of explaining
our own behavior – perhaps, as Nisbett and Wilson suggest, theoretical inference – in cases where introspection finds nothing there.
Of course, it would be very difficult to design an experiment to test
the accuracy of introspection in what I’m assuming are the
20
circumstances optimal for its operation. We would have to have
circumstances in which the agent has a reason, and we know
what it is. And it’s hard to see how those conditions could be operationalized; it’s much easier to set things up so that the agent has
to be wrong. But of course, scientists should not be looking under
the corner lamppost for watches dropped in the middle of the
street.
I called the stocking comparison set-up a case of “degraded
conditions.” The degradation here is the absence of any reason
in the agent’s mind for introspection to detect. Other kinds of suboptimality include hard cases – cases where there are or might be
rational bases for decision, but these bases do not readily determine the best course – and marginal cases – cases where there
are non-rational factors, such as emotional responses, that feed
into the agent’s decision. Asking for an agent’s reasons in any of
these circumstances is likely to provoke a state of mind similar
to those that are called cases of dumbfounding in the literature
on the psychology of moral judgment – cases in which subjects
report strong moral judgments for which they offer no compelling
moral justification. Accordingly, I would make a similar criticism
of work that attempts to draw inferences about our ordinary
moral reasoning from the responses subjects make in such
cases: It is methodologically unsound to draw conclusions about
our ordinary moral decision making from post hoc rationalizations
of judgments about hard or marginal cases.
Maybe it helps to be conscious, after all
doi:10.1017/S0140525X13000630
Roy F. Baumeister,a Kathleen D. Vohs,b and
E. J. Masicampoc
a
Department of Psychology, Florida State University, Tallahassee, FL 32306–
4301; bCarlson School of Management, University of Minnesota, Minneapolis,
MN 55455; cDepartment of Psychology, Wake Forest University, WinstonSalem, NC 27109.
baumeister@psy.fsu.edu
vohsx005@umn.edu
masicaej@wfu.edu
http://www.psy.fsu.edu/faculty/baumeister.dp.html
http://www.carlsonschool.umn.edu/faculty-research/vohsx005/
Kathleen_D_Vohs.aspx
http://www.wfu.edu/~masicaej/
Abstract: Psychologists debate whether consciousness or unconsciousness
is most central to human behavior. Our goal, instead, is to figure out how
they work together. Conscious processes are partly produced by
unconscious processes, and much information processing occurs outside
of awareness. Yet, consciousness has advantages that the unconscious
does not. We discuss how consciousness causes behavior, drawing
conclusions from large-scale literature reviews.
Science proceeds by approaching the truth gradually through successive approximations. A generation ago, psychologists began to
realize that the conscious control of action had been overestimated, and many began to search for unconscious processes.
The pendulum has now swung the other way, as Newell &
Shanks (N&S) have shown in their valuable corrective to the
excesses of that view. Now it is the unconscious effects that
have been overestimated.
We think the way forward for psychological theory is to stop
pitting conscious against unconscious and instead figure out how
the two work together. Plainly, there is plenty of processing of
information that occurs outside of awareness. Likewise as
plainly, consciousness has advantages and can accomplish things
that the unconscious cannot.
N&S propose their lens model with multiple stages of processing. They rightly criticize the tendency to claim that some
outcome is unconscious based on showing that only one of the
five steps is unconscious. Still, the fact that some steps are
unconscious is a genuine contribution that will need to be
included in the eventual, correct account of human thought and
action.
Recently, some theorists have become increasingly bold in
asserting that consciousness is an epiphenomenon with no
impact on behavior. We have been skeptical that such a
complex, advanced phenomenon as human conscious thought
would evolve without conferring vital advantages. Inspired by
this skepticism, we conducted an extensive review of experimental
evidence for the conscious causation of behavior (Baumeister
et al. 2011). We searched for experiments in which the independent variable was a conscious thought or other conscious event
and the dependent variable was overt behavior. By the logic of
experimental design, such studies prove causation. We found a
wide assortment, leading to our conclusion that the evidence for
conscious causation was extensive, diverse, and undeniable.
Still, none of the evidence we found ruled out important, even
essential contributions by unconscious processes. We speculated
that there may well be no human behaviors that are produced
entirely by consciousness (and likewise few produced entirely by
unconscious processes). If all behavior is indeed produced by
both conscious and unconscious processes, then it is imperative
to understand both types and how they interact.
In fact, we think that conscious processes are themselves produced in part by unconscious processes. Baumeister and Masicampo (2010) concluded that consciousness is best considered a
place where the unconscious constructs meaningful sequences
of thought. This is linked to evidence that, for example, the unconscious processes single words but not sentences and paragraphs
(see Baars 2002). Likewise, logical reasoning deteriorates
sharply when consciousness is preoccupied and improves when
engaged (DeWall et al. 2008). Logical reasoning requires
putting together complex sequences of ideas while ruling out
other possible sequences, and that may too big a job to do effectively without consciousness.
What else is consciousness good for? We are wary of making
assertions that something absolutely cannot be done unconsciously – but perhaps that is not necessary. After all, the capacity
for conscious thought would have been favored by natural selection simply on the basis of doing something better or more
thoroughly than unconscious processes, even if the unconscious
could occasionally do something along the same lines well
enough to produce an experimental finding. For example, one
recent paper has proposed that the unconscious can do some
arithmetic (Sklar et al. 2012). Even if this finding could measure
up to the methodological standards proposed by N&S, we think
that is hardly a reason to dismiss the usefulness of conscious
thought for mathematical work. Does anyone seriously think
that a student could pass a college math test without conscious
thought?
Key themes from our survey of experimental findings on conscious causation (Baumeister et al. 2011) included the following:
Conscious thoughts integrate across time. That is, conscious
thought permits the deliberate combining of past and future
into causing present behavior, as well as helping present cognitions to cause future behavior, and probably other combinations.
Most animals live largely in the present, whereas the stupendous
success of the human race has benefited immensely from integrating across time – for which conscious thought probably deserves
much of the credit.
Conscious thought also helps translate abstract principles into
specific behaviors. Humankind has benefited from moral principles, legal rules, economic calculations, application of scientific
and mathematical principles, and other sorts of general, abstract
understandings. The unconscious may be effective at processing
highly specific stimuli and responses, but without conscious
thought, it may lose most of the benefits of abstract principles
for guiding behavior.
We also found that conscious causation of behavior was typically
found in situations involving multiple possibilities, such as for
deliberating among multiple possible courses of action by considering their likely consequences. Consciousness enables the person
to mentally simulate nonpresent realities, including possible
sequences of future events. In our view, the jury is still out on
whether the act of choosing is conscious or unconscious – but
most decisions will be considerably more effective insofar as one
uses conscious thought to ponder what each possible action will
produce and the desirability of downstream consequences.
Last, and perhaps most important, consciousness is highly
useful for communication. We have not heard even the most
assertive critics of conscious thought claim that a person could
carry on a conversation unconsciously. Indeed, we think that the
evolutionarily decisive advantages of conscious thought are not
to be found in private, solipsistic ratiocination but rather in its contribution to communication (Baumeister & Masicampo 2010).
Humankind’s biological strategy for surviving and reproducing
has been centrally based on sharing information and coordinating
joint performance. Although the principle that thinking is for
doing has been widely considered sacrosanct since first asserted
by William James (1890), we propose that a viable partial alternative is that conscious thinking is for talking.
In sum, we applaud N&S for pushing the field forward. Conscious thought is a vital part of human life. We think the view
that humans could operate effectively without conscious thought
will soon be regarded as quaint and naïve.
The problem of consciousness in habitual
decision making
doi:10.1017/S0140525X13000642
Javier Bernacer,a Gloria Balderas,a Ivan Martinez-Valbuena,a
Maria A. Pastor,a,b and Jose Ignacio Murilloa
a
Mind–Brain Group, Institute for Culture and Society (ICS), University of
Navarra, 31009 Pamplona, Navarra, Spain; bNeuroimaging Laboratory,
Neurosciences Section, Center for Applied Medical Research, University of
Navarra, 31008 Pamplona, Navarra, Spain.
jbernacer@unav.es
gcbalderas@gmail.com
imartinez.18@alumni.unav.es
mapastor@unav.es
jimurillo@unav.es
http://www.unav.es/centro/cultura-y-sociedad/mind_brain
Abstract: Newell & Shanks (N&S) carry out an extremely sharp and static
distinction between conscious and unconscious decisions, ignoring a
process that dynamically transfers decisions and actions between the
conscious and unconscious domains of the mind: habitual decision
making. We propose a new categorisation and discuss the main
characteristics of this process from a philosophical and neuroscientific
perspective.
Newell & Shanks (N&S) establish the elements of a decision by
using the lens model (Brunswik 1952). According to their framework, decisions can be either conscious or unconscious: In the
former, all five stages of the lens model are supervised by consciousness, whereas in the latter at least one of the five elements is unconsciously performed. In our opinion, this is an extremely sharp
distinction that leaves out of the picture aspects of a crucial importance in action selection, such as habits or habitual decision making.
Human agents make many decisions every day, some of which
are fully unnoticed. Considering the role of consciousness in the
course of decision making, we propose three categories in which
a decision can be included: (1) conscious decisions, (2) retrospective attributions to unconscious behaviour, and (3) non-conscious
but controlled decisions. The first type refers to deliberative
decisions, which are made when facing a problem that requires
a high cognitive load, especially involving a novel situation. Retrospective attributions are actions performed under low or nonexistent levels of consciousness, and whose meaning is attributed
21
a posteriori according to its effect; they include the illusion of conscious will (Wegner 2004), whose extreme interpretation suggests
that consciousness is just a result of brain function. In our opinion,
N&S only consider these two categories throughout their review,
skipping the third type: rationally motivated decisions that are
unconsciously performed but continuously open to conscious
control. This omission disregards the dynamic aspect of decision
making, which is contributed by learning. To illustrate the importance the third category of our classification has in human decision
making, we will focus on technical habits.
Technical habits such as driving, painting, playing an instrument, or handwriting involve a high number of “decisions” –
understood as the selection of a cognitively motivated course of
behaviour – in their performance. Considering the lens model,
most of them are unconscious in every stage of the process,
since the agent does not even realise that a decision has been
made. However, when a difficulty or something novel appears,
consciousness immediately regains control of the process. At
this point, it becomes evident the importance of previous learning
and the dynamic aspect of decision making. Through habit learning, the agent transfers some particular actions from a conscious to
an unconscious performance, without losing the capacity to consciously intervene at any time. Neuroscientific literature often
identifies habits with automatic behaviour, interpreting them as
a simple stimulus–response pair (see Seger & Spiering [2011]
for a review). As it has been recently proposed (Bernacer &
Gimenez-Amaya 2012), we believe that this view considers only
the unconscious aspect of habit’s performance and ignores the
continuous access that consciousness has to regulate the
process. In fact, we propose that habitual decision making
should be considered an overall conscious rather than an unconscious operation, because it is consciously initiated and accessible
to conscious control at all times. Another reason to support habitual decision making as an overall conscious process can be found
in its comparison with instincts (Brigandt 2005; Lorenz 1950).
From the point of view of ethology, both – learned habits and
innate instincts – include unconscious stages that contribute to a
final conscious and cognitive goal. However, there is an important
distinctive feature between them: When an instinct is abruptly
interrupted, the whole operation ends. By contrary, when a
non-conscious but controlled process is suspended, it is substituted by the conscious performance of the action.
We acknowledge new difficulties arise when including habits in
decision making. The first of them is the most basic: how to delimitate a decision in this scenario. When a pianist is improvising, is
he or she deciding to play each note, each chord of the whole
melody? In our opinion, as it happens with actions, a decision
has to be defined by its aim (Murillo 2011). Thus, in this particular
example, the pianist decides to improvise. However, denying that
this conscious decision – improvising – initiates many other nonconscious decisions – each key stroke, which has become non-conscious through habit learning – is a partial view of the process. For
that reason, we believe that a more flexible understanding of the
role of consciousness in decision making may be beneficial to have
a more accurate view of the whole process.
In addition, it should also be considered the experimental difficulties to assess habit learning in the laboratory. The acquisition of
a habit is usually measured by the decrease in the number of
errors and the reduction of serial reaction times when repeatedly
performing a sequence of movements (Aznarez-Sanado et al.
2013). Furthermore, experiments usually include a second task
to be performed at the same time as the “automatic behaviour”
(Wu et al. 2004). This approach involves the additional difficulty
of distinguishing which brain activity pattern corresponds to
which task. Although the neural bases of consciousness is a
highly debated matter, cognitive cortical and subcortical areas
had been associated to conscious performance of actions,
whereas motor and premotor regions of the cortex, the posterior
striatum, and cerebellum are considered to be in charge of subconscious processes. A recent study assessed early motor learning
22
in the course of a continuous motor task by using functional magnetic resonance imaging. Brain activation progressively decreased
in prefrontal cognitive regions and, conversely, increased in
motor-related brain areas. Interestingly, an enhanced connectivity
between the posterior putamen – a motor-related region – and the
hippocampus was found, which supports the hypothesis of interactive cortico-subcortical memory systems in the course of learning (Fernandez-Seara et al. 2009). To the best of our knowledge, a
reliable neuroscientific study of technical habits is yet to be
achieved. For that reason, we believe it is extremely important
to lay solid theoretical foundations for an adequate experimental
approach.
To conclude, N&S give an interesting overview about the lack
of reliable evidence to demonstrate the role of unconscious
drivers in decision making. However, their static framework
does not allow the inclusion of habit learning, a fundamental
element in decision making that involves a continuous transference of actions between the conscious and unconscious domains
of the mind.
Unconscious influences on decision making
in blindsight
doi:10.1017/S0140525X13000654
Berit Brogaard, Kristian Marlow, and Kevin Rice
Department of Philosophy and Center for Neurodynamics, University of
Missouri, St. Louis, MO 63121.
brogaardb@gmail.com
www.beritbrogaard.com
me@kristianmarlow.com
www.kristianmarlow.com
kevin.rice0620@gmail.com
Abstract: Newell & Shanks (N&S) argue that an explanation for blindsight
need not appeal to unconscious brain processes, citing research indicating
that the condition merely reflects degraded visual experience. We reply
that other evidence suggests blindsighters’ predictive behavior under
forced choice reflects cognitive access to low-level visual information
that does not correlate with visual consciousness. Therefore, while we
grant that visual consciousness may be required for full visual
experience, we argue that it may not be needed for decision making and
judgment.
Blindsight – the ability that some people with striate cortical
lesions have to make solid predictions about visual stimuli in the
absence of any reported visual awareness – is a paradigmatic
example of unconscious perceptual influence on conscious behavior. Newell & Shanks (N&S) are skeptical about the influence of
unconscious processes on decision making in blindsight, given
that blindsight seems to have alternative explanations that do
not appeal to unconscious processes. To back up their claim,
they cite research indicating that blindsight may just be degraded
visual experience (Campion et al. 1983; Overgaard 2011; Weiskrantz 2009). A review of further empirical and theoretical
work in the area of blindsight reveals that N&S’s conclusions primarily based on Overgaard’s (2011) findings on blindsight are premature (Brogaard 2011a; 2011b; 2011c; 2012a; 2012b; Cowey
2001; Hardin 1985; Heywood & Kentridge 2003; Stoerig &
Cowey 1992).
Using a multipoint awareness measurement (PAS), Overgaard
(2011) shows that the reported degree of conscious experience
in blindsight correlates with the blindsight subjects’ predictive
success in visual tasks. These considerations appear to indicate
that it is conscious perception, rather than unconscious processes,
that is responsible for blindsighters’ predictive behavior. For these
reasons, N&S conclude that we need not appeal to unconscious
processes in explaining blindsighters’ success in decision
making. However, Overgaard’s studies do not completely rule
out that the reported awareness that corresponds to predictive
success in blindsight is awareness associated with the higher-order
predictive act rather than genuine visual awareness. The subjects
may not have the ability to distinguish between being aware of
thoughts or judgments and being visually aware of a visual
stimuli (Brogaard 2011a; 2012b), in which case the PAS studies
don’t show that unconscious influences are not needed for the
predictive behavior.
Further, it’s only under forced-choice paradigms that blindsighters are in a position to make predictions about visual stimuli. This
suggests that, unlike neurotypical individuals, blindsighters lack
full visual awareness in their access to visual information. Even if
Overgaard’s findings are granted, blindsighters’ degraded conscious
experience may reflect cognitive access to low-level information
deriving from visual, unconscious processes taking place in the
retina and the lateral geniculate nucleus (LGN) (and perhaps to
some extent in V1). Likewise, the correlation between accurate
responses and subjective reports of consciousness may only reflect
the subject’s level of cognitive access to information derived from
visual, unconscious processes. If this is the case, then visual processes that do not correlate with visual awareness may well be at
work in blindsighters’ decision making, even if the information
that is unconsciously processed can be cognitively accessed in
forced-choice paradigms. In the light of such observations, N&S’s
conclusion to the effect that blindsighters’ predictive behavior is
not influenced by unconscious processes does not hold up.
Theories and research about color processing, together with blindsight data, provide additional evidence that blindsighters must be
tapping into unconscious processes in order to make successful predictions about visual stimuli (Brogaard 2011a; 2011c). Although
blindsight patients have lesions to striate cortex, they can still
process information from opponent processes in the retina and the
LGN. The Hering–Hurvich–Jameson opponent-process theory is
the most popular explanation of how the brain interprets signals
from the cones and rods in the retina (Brogaard 2012a; Hardin
1985). Three types of cones (L for long, M for medium, and S for
short) are responsible for detecting chromatic (colored) daylight,
while rods are responsible for detecting achromatic (black-white)
nightlight. Because the three types of cones overlap in the wavelengths of light they record, color is processed via three opponent
channels manifested in bipolar cells. These cells measure differences
between red (L) and green (M), blue and yellow (the differences
between L plus M and S), and black and white (the sum of L and
M). For example, when the activity of M exceeds L, the resulting
perceived color is green. When the activity of S exceeds the joint
activity of L and M, the perceived color is blue.
Blindsighters can detect these outcomes, and yet they have
degraded function of the double opponent cells of striate cortex
and in areas upstream from the primary visual cortex. As a
result, they cannot detect the standard dimensions of color, such
as brightness, saturation and hue. Even if damage to V1 does not
prevent weak visual experiences, it does preclude full visual experience of colors (Brogaard 2011a; 2011b; 2011c; 2012b; Hardin
1985; Stoerig & Cowey 1992). Likewise, people with achromatopsia, which results from a defect to the V4/V8 color complex, lack
conscious color experience of the full range of colors (Brogaard
2011a; Heywood & Kentridge 2003; Heywood et al. 2001).
Despite reporting no or only weak awareness of visual stimuli,
blindsighters are able to act reliably in response to wavelengths
of light presented to their blind field region, or scotoma. For
example, Stoerig and Cowey (1992) showed that under forced
choice, three blindsighters were able to discriminate among narrowband wavelength stimuli despite lacking experience of any
conscious visual stimuli. A more recent study showed that inhibiting activity in V1 via transcranial magnetic stimulation (TMS) can
result in the absence of color awareness in neurotypical individuals
despite the retained ability to discriminate among wavelengths in
a forced-choice paradigm (Boyer et al. 2005).
Studies have also shown that blindsighters can be trained to
detect wavelengths in the absence of conscious color experience
(Bridgeman & Staggs 1982; Stoerig & Cowey 1992; Zihl 1980;
Zihl & Werth 1984). Stoerig (1993) presented the case of
patient F.S., who initially, for several years, showed no statistically
significant detection of visual stimuli. However, his performance
eventually began to improve. The trainability of blindsighters provides further evidence that blindsight patients do in fact lack conscious visual experience. If blindsighters did have degraded visual
perception, we should expect subjective reports made by trained
subjects to reflect greater visual awareness in correspondence to
their improved ability to accurately respond to visual stimuli.
This, however, is not the case. Trained blindsighters consistently
report no or only weak conscious visual experiences. Although
patients such as F.S. consistently report a lack of conscious
visual experience, trainability suggests that these blindsighters
learn to report on other information that may be easily available
for conscious access. In these cases, blindsighters presumably
act on the physical bases of color experience despite lacking
normal conscious visual experience.
Thus, while visual consciousness may be needed for full visual
perception, certain cases show that visual consciousness may not
be needed for decision making and judgment.
Cognitive access to information processed unconsciously
appears to suffice for reliable decision making in blindsight. In
these cases visual information that does not correlate with visual
consciousness influences predictions about features of the visual
stimuli present in the subject’s blind field.
Unconscious influences on decision making:
Neuroimaging and neuroevolutionary
perspectives
doi:10.1017/S0140525X13000666
Samantha J. Brooks and Dan J. Stein
Department of Psychiatry and Mental Health, Groote Schuur Hospital,
University of Cape Town, Observatory 7925, South Africa.
Samantha.brooks@uct.ac.za
dan.stein@uct.ac.za
http://www.health.uct.ac.za/departments/psychiatry/about/
Abstract: Newell and Shanks provide a useful critique on unconscious
decision making. However, they do not consider an important set of
functional brain imaging studies of unconscious processes. Here we
review briefly the relevant brain imaging and psychobiological literature
and its implications for understanding unconscious decision making.
Newell & Shanks (N&S) argue that priming studies, although
promising, are flawed by inadequate self-report measures of
awareness. However, functional magnetic resonance imaging
(fMRI) subliminal priming studies (which are perhaps more
objective than self-report measures) should also be taken into
account. In such work, differences in brain activation are observed
between stimuli that are consciously versus subliminally perceived. Thus, in a recent meta-analysis by Brooks et al. (2012)
of subliminal fMRI studies, it is notable that non-conscious
salient stimuli robustly activate the amygdala and hippocampus –
brain regions linked to emotional arousal (Costafreda et al. 2008)
and memory (Garcia-Lazaro et al. 2012). In the meta-analysis,
most studies used highly salient subliminal emotional faces, but
subjects reported a lack of awareness of these faces, and there
was no activation of the fusiform gyrus, a region of the occipital
cortex well-known to be involved in conscious recognition of
faces (Weiner & Grill-Spector 2012). Furthermore, when comparing brain responses to the same affective stimulus, presented
either with or without awareness, our data indicate that there is
a switch from insular cortex (associated with conscious interoceptive awareness) to posterior cingulate cortex activation (associated
with default mode network and auto-affective processing), and
that the anterior cingulate cortex (ACC) activates in response to
both levels of perception (Meneguzzo et al., under review). The
23
ACC is implicated in decision making, particularly with respect to
conflict monitoring and error detection, as well as cognitive
control (Carter & van Veen 2007), perhaps especially during
high-anxiety states (Ursu et al. 2003). Thus, decision-making processes may be mediated by areas such as the ACC, and they are
influenced by non-conscious affective states (Panksepp 2005).
Subliminal fMRI findings are consistent with a view that automatic activation of affective processes probably guides evolutionarily advantageous decision-making mechanisms (Panksepp 2005;
2011a). Such affective processes have evolved to respond efficiently
to environmental threats and appetitive stimuli, are mediated by a
range of neuroendocrine and neurotransmitter systems, and
operate at least partially outside of awareness (Damasio 2010; Kahneman 2011; Le Doux 1996; Panksepp 2005). The involvement of
evolutionarily ancient mesolimbic brain regions, such as the amygdala, hippocampus, and striatum, as well as brain stem structures
(e.g., the periaqueductal gray) during affective states, underlines
their importance in adaptation (Alcaro & Panksepp 2011; Panksepp
2011b, Stein et al. 2006). The activation of these core brain regions
presumably occurs with less conscious and less cortical processing
in non-human species, and yet it is still meaningful in the
decision-making process. Consistent with the findings emerging
from the imaging of subliminal processes, neuroimaging work
during exposure to threat cues highlights the role of similar brain
regions. For example, Mobbs et al. (2007) demonstrated that the
imminence of threat shifts brain activation from prefrontal
regions to periaqueductal gray, particularly in terms of the degree
of dread and lack of confidence in the decision to escape, supporting the notion that automatic brain processes are more prominent
under highly arousing affective states.
Most subliminal fMRI studies convincingly assess the lack of
awareness with a “forced choice” task post hoc (choosing
between a novel and a previously subliminal image), which is
further corroborated by the participants’ inability to recollect
verbally any subliminal affective stimulation during the scan
(e.g., Brooks et al. 2012). Future fMRI studies using subliminal
paradigms must ensure, as N&S emphasize, awareness checks
that enable reliable self-report, are relevant to the task, immediately precede the subliminal stimulus, and are sensitive to the subliminal cues. Subliminal fMRI studies may provide an attractive
methodology for overcoming some of the discrepancies outlined
by N&S in the field of subliminal priming research. In line with
N&S’s arguments, given that subliminal fMRI studies have not
yet probed decision making, this may be a particularly useful component of such work. An evolutionary theoretic framework may be
useful in future work to provide an ecologically valid approach to
choosing and analyzing relevant cues. Connectivity analyses in
subliminal fMRI studies may be useful in probing not only localized brain function, but also in developing network models of
the neural circuitry of non-conscious processes. Finally, rigorous
studies of different subliminal cues may well be informative in
the study of a spectrum of psychopathologies (Stein 1997).
Unconscious influences of, not just on,
decision making
doi:10.1017/S0140525X13000678
Géraldine Coppin
The John B. Pierce Laboratory, School of Medicine, Yale University,
New Haven, CT 06519; Department of Psychiatry, School of Medicine,
Yale University, New Haven, CT 06511.
geraldine.coppin@yale.edu
http://jbpierce.org/neuropsychology-and-physiology-of-flavor-andfeeding/
Abstract: This commentary focuses on the bidirectional links between
unconscious influences and decision making. In particular, it examines
24
the extent to which awareness is (not) necessary to the impact of
decisions on psychological processes such as preferences. This analysis
might help researchers to gain an extended perspective of Newell &
Shanks’ (N&S’s) concerns regarding the role of unconscious influences
in theories of decision making.
Newell and Shanks’ (N&S’s) article covers several major domains
of research in psychology in order to question the idea that unconscious influences play a major role in decision-making processes.
They do not, however, make any mention of the extent to which
decisions could subsequently lead to unconscious influences.
Nonetheless, it seems important to discuss this aspect, as it
echoes the concerns raised by N&S in a directly related matter.
Unconscious influence of decisions – the case of
preferences. In principle, decisions may exert unconscious influ-
ence on virtually any psychological process. We will here restrict
our analysis on the influence of decisions on preferences. This
influence has been extensively replicated (e.g., Harmon-Jones &
Mills 1999), although the exact nature of the mechanisms at
play has recently been debated (e.g., Chen & Risen 2010).
Here, we focus on a different aspect: the level of processing
(unconscious vs. conscious) that is required for the modulation
that decisions can exert on preferences, which is controversial.
The assumption of awareness. The influence of decisions on
preferences can be summarized as follows: After a decision
between two similarly liked alternatives (and only in those
instances), the chosen one is rated as more pleasant and the
rejected one as less pleasant (Brehm 1956). Classical authors
(have argued that the impact of decisions on preferences requires
awareness (Festinger & Walster 1964; Wicklund & Brehm 1976).
Thus, one is assumed to be aware that one’s choice is in conflict
with the desirable aspects of the rejected option and with the
undesirable aspects of the chosen one, both options being similarly liked. The awareness of this conflict is assumed to lead to
the need for cognitive dissonance reduction (Festinger 1957),
that is, in this case, readjusting one’s preferences to be more in
line with one’s choice – by devaluing the rejected option and/or
valuing the chosen one more positively.
Distraction from awareness. Lyubomirsky and Ross (1999,
study 3) have shown that distracting participants reduced postchoice preference modulation. In the same vein, Allen (1965)
showed that the modulation of preferences following a difficult
choice was less pronounced when postchoice time was filled
with an extraneous cognitive task. As put by McGregor et al.
(1999), “individuals seem to have a remarkable capacity for avoiding awareness of inconsistencies unless their noses are quite vigorously rubbed in them” (p. 331). In these experimental settings,
decisions do not impact preferences as much when awareness of
the dissonance (which is assumed to drive this impact) can be
avoided. In other words, awareness appears to be required in postchoice preference modulation.
How can this evidence be related to N&S’s discussion? Although
the design of these experiments is different from the deliberationwithout-attention paradigm, the two criteria1 that need to be met
in this paradigm can be argued to also apply here. Criterion 1 was
met in both experiments: Distracting participants led to significantly
lower postchoice preference modulation than when participants
were not distracted. However, just as in the case of the deliberation-without-attention paradigm, criterion 2 was not so well
accounted for. In Lyubomirsky and Ross’s (1999) experiment, criterion 2 could not be assessed: The second measurement was
made 8 minutes after the decision, but no immediate condition was
run. In contrast, Allen’s (1965) experiment includes four conditions:
2 and 8 minutes of cognitive rest, as well as 2 and 8 minutes of cognitive activity. Preference modulation at 8 minutes in the cognitive
activity condition was lower than the one at 2 minutes (independently
of the condition), which supports the idea that awareness is a necessary requirement for preference modulation by choice.
Recent evidence against the necessity of awareness. Recent
empirical data have, however, questioned the assumption of the
necessity of conscious processes in the impact that decisions can
exert on preferences. These experiments have investigated this
impact in three ways. First, patients with anterograde amnesia
were asked to make decisions, which they were, due to their condition, unable to consciously recall (Lieberman et al. 2001).
Second, healthy participants were asked to make several decisions.
Their conscious memory for these decisions was measured in an
incidental memory task at the end of the experiment. This makes
it possible to dissociate the impact of consciously forgotten decisions
from the impact of consciously remembered decisions on preferences (Coppin et al. 2010). Third, participants were presented
with decisions that they had not in fact made themselves. This
third approach has been applied to both rhesus monkeys (Egan
et al. 2010) and human participants (Sharot et al. 2010). In all of
these three experimental contexts, participants’ lack of decision
memory makes it highly unlikely that experimental demand
effects might be at play. Unconscious influences of decision on subsequent preferences were nonetheless still reliably demonstrated.
These experimental settings control the potential pitfalls of experimental demand or social desirability effects (i.e., reliability criteria)
by targeting the conscious memory of the decision. Thus, although
participants’ attention was not, per se, diverted away from the experiment’s hypothesis, participants could not be consciously acting in a
way that fits the experimenter’s hypothesis. As recommended by
N&S, the most recent literature on the (potentially unconscious)
impact of decisions on preferences has moved away from the deliberation-without-attention framework by replacing it by this type of
experimental setting. The similarity between the literature of the
unconscious influences on decisions and the literature on the unconscious impact of decisions is striking in this respect.
Conclusion. The debate regarding the extent to which decisions
can exert an unconscious influence on preferences echoes the discussion of N&S on the extent to which unconscious influences are
at play in decisions. Experiments tailored to investigate unconscious influences on or of decisions might consequently equally
consider N&S’s discussion. By adopting this perspective, future
experiments might lead to fascinating debates regarding N&S’s
conclusion that unconscious influences “should not be assigned
a prominent role in theories of decision making and related behaviors” (target article, sect. 1).
ACKNOWLEDGMENTS
This work was supported by a Swiss National Science Foundation fellowship
for prospective researchers to Géraldine Coppin (PBGEP1–139853). The
author would like to thank Bastiaan Oud for insightful comments.
NOTE
1. “Demonstration of the benefit of unconscious thought on choice
requires two criteria to be satisfied. First, choices following distraction
need to be significantly better than those following deliberation, and
second, they need to be better than those following an immediate
decision” (target article, sect. 3.1). Note that if one assumes that awareness
is required in postchoice preference modulation, predictions are the exact
opposite of the ones quoted. Thus, in the experiments described here,
postchoice preference modulation following distraction needs to be significantly lower than the one following deliberation (criterion 1), and it needs
to be lower than the one measured right after the decision (criterion 2).
Newell and Shanks’ approach to psychology is
a dead end
doi:10.1017/S0140525X1300068X
Ap Dijksterhuis, Ad van Knippenberg, Rob W. Holland, and
Harm Veling
Department of Social Psychology, Behavioural Science Institute, Radboud
University Nijmegen, 6525 HR Nijmegen, The Netherlands.
a.dijksterhuis@psych.ru.nl
a.vanknippenberg@psych.ru.nl
r.holland@psych.ru.nl
h.veling@psych.ru.nl
Abstract: Newell & Shanks (N&S) criticize theories on decision making
that include unconscious processes. To the extent that their own
perspective becomes apparent, however, it is dated, implausible, and at
odds with the major developments of the past decades. Their
conclusions are, at least for research areas we feel entitled to evaluate,
based on a biased sampling of the literature.
Although we think that the development of our field benefits from
critical discussion, the scientific contribution of the current target
article escaped us. It is neither a theoretical article, as it lacks a
theory, nor is it a good review article, because it is biased and selective. In our commentary, we first focus on the lack of theory and
subsequently on the misleading treatment of the literature.
In our view, the goal to incorporate processes that we are not conscious of in our theories was at least partly sparked by the wish to make
our field more mature. Making psychology compatible with philosophy and neuroscience and getting rid of homunculi was, and is, a
worthwhile endeavor. This goal led to a burgeoning number of theories and models on unconscious processes – in which there usually
is a role for conscious processes, though a modest one – and critical
responses from colleagues who maintain that all important psychological processes are guided by consciousness.
Newell & Shanks (N&S) defend an extreme version of the latter
position, but fail to provide a reasonable alternative. If conscious
thoughts do not have unconscious precursors, as N&S seem to
believe, then where do they come from? Unless one believes
that consciousness freely hovers in the air, or is in direct contact
with the gods, claiming that psychological processes start in consciousness without further ado does not make sense. The house
N&S try to build stands on scientific quicksand.
N&S’s lack of theory is all the more disappointing in light of the
recent scientific progress on the distinction between attention and
consciousness, and on the relation between conscious and unconscious processes (Dehaene et al. 2006; Dijksterhuis & Aarts 2010;
Koch & Tsuchiya, 2006; Lamme, 2003; Wegner & Bargh, 1998).
Unfortunately, they completely ignore this literature. Perhaps needless to say, stating “it’s all starting in consciousness” may have been satisfactory to Descartes, but it doesn’t work in the twenty-first century.
The target article also falls short of being a comprehensive
review. It was mystifying to us why one would criticize the work
by Libet (1985) without at least mentioning groundbreaking
recent additions (e.g., Soon et al. 2008), or why one would question prime-to-behavior effects on the basis of a few nonreplications without acknowledging the fact that such effects have
been reported in well over a 100 papers.
Unavoidably, we read the section on unconscious-thought theory
(UTT) attentively (sect. 3.2). We found the reasoning often flawed
and the degree of cherry picking too extreme. However, we do
agree with some observations of N&S. The strength of the initial evidence for unconscious-thought effects (UTEs) was indeed rather
weak. The strong early claims such as the one “to leave decisions
to the unconscious” (Dijksterhuis et al. 2006a) were, in retrospect,
naïve. Finally, UTEs have been proven far from robust. It is indeed
likely that there is a publication bias, but we all know this is a general
problem, at least in psychology. Show us a psychological phenomenon studied in over 30 experiments, convince us there is no publication bias, and we will send an expensive bottle of wine.
That being said, the way N&S treat the UT literature does not
do justice to the field. To begin with, they pre-emptively formulate some arbitrary inclusion criteria that allow them to discard
dozens of experiments supporting UTT. On top of that, they
ignore many papers fully compatible with their own criteria that
do support UTT (e.g., Ham & van den Bos 2010a; 2010b; 2011;
Ham et al. 2009; Handley & Runnion 2011; McMahon et al.
2011; Messner & Wänke 2011). An emphatic reader will understand that for people who have contributed to unconsciousthought research, reading this section was a tad discouraging.
N&S also suggest alternative explanations. They cite Newell
et al. (2009), who asked participants to indicate their own attribute
weights after they made their choice (instead of weights
25
predetermined by the experimenter). Using these idiosyncratic a
posteriori weights, choices in the conscious thought condition
were just as good as in the unconscious thought condition.
Obviously, participants are capable of generating post hoc
weights that justify their previous choice. Usher et al. (2011)
measured participants’ idiosyncratic attribute weights before the
decision task and found the unconscious thought advantage predicted by UTT. N&S should have concluded that this completely
refuted their idiosyncratic weights explanation, but they refrained
from doing so because Usher et al.’s study did not satisfy their
inclusion criteria (which are obviously irrelevant for his particular
conclusion). Furthermore, they mention the possibility that participants make decisions before they can engage in unconscious
thought, but fail to say that this explanation has already been
ruled out in studies they conveniently disregarded (e.g., Bos
et al. 2008).
We are optimistic about unconscious thought research, despite
the clear limitations alluded to above. In a recent meta-analysis
(Strick et al. 2011), moderators were found that led the UTE to
be replicated with greater ease. Furthermore, new additions to
the literature, such as a paper integrating UTT with fuzzy trace
theory (Abadie et al. 2013), and a paper reporting the first
fMRI evidence for UT (Creswell et al. 2013) have appeared
recently.
More generally, we strongly argue that consciousness and conscious decisions are best understood by their relation to unconscious processes. The most sensible approach to learn about
conscious decisions is thus to consider higher cognitive operations
as unconscious, and test what (if anything) consciousness adds
rather than the other way around (e.g., Dijksterhuis & Aarts
2010; van Gaal et al. 2011; Zedelius et al. 2012). Although we
surely agree that the road to progress in this field is rocky, focusing
on consciousness without understanding its unconscious precursors is a dead end.
The presumption of consciousness
doi:10.1017/S0140525X13000691
Jonathan St. B. T. Evans
School of Psychology, University of Plymouth, Plymouth PL4 8AA,
United Kingdom.
jevans@plymouth.ac.uk
http://www.plymouth.ac.uk/pages/dynamic.asp?page=staffdetails&id=
jevans&size=l
Abstract: Throughout this article the authors presume – without
justification – that decision making must be a conscious process unless
proved otherwise, and they place an unreasonably strict burden of proof
on anyone wishing to claim a role for unconscious processing. In
addition, I show that their arguments do not, as implied here, impact
upon contemporary dual-process theories of reasoning and decision
making.
There are two aspects of this article upon which I would like to
comment. The first is the extraordinary presumption of consciousness that runs through the entire piece, and the second is the misconceived attack on dual-system theories that appears towards the
end.
Newell & Shanks (N&S) hold a strong presumption of consciousness with which they view all evidence. We can understand
this by analogy to the presumption of innocence enshrined in
those criminal justice systems based upon English common law.
A decision process is conscious until proven unconscious; the
burden of proof lies with those wishing to argue for unconscious
decision making, and they must prove their case beyond reasonable doubt. Take, for example, the work on multicue learning
and judgment reviewed early in the article. As the authors
26
acknowledge, there are a number of studies, including those conducted in my own laboratory, which show that people can learn to
make such judgments without being able to describe the basis for
their choices as explicit knowledge. But using their presumption
of consciousness and a very strong set of criteria (N&S Table 1),
they nevertheless argue that the case for any of the actual learning
or decision making being unconscious is not proven.
I ask the reader for the moment to adopt the opposite presumption: that decision making is unconscious unless proven beyond
reasonable doubt to be conscious. How much harder would it
be for the advocates of conscious reasoning to prove their case
on this evidence than it was for their opponents under the opposite presumption? Even taking the civil law criterion of “balance of
probabilities,” would the evidence again not clearly favour the
advocate of the unconscious? It would obviously be easier for all
of us to place a much stronger burden of proof on our theoretical
opponents than we apply to ourselves, but we would clearly need
to have a very strong justification for so doing. None is provided by
the authors. The only basis for their presumption that I can see is
common sense or folk psychology, as the everyday belief in conscious decision making is indeed widespread. In support of this,
they do in places state or imply a default “intuitive” position of
conscious thought.
The definition of conscious decision making that appears
implicit in the authors’ writing is “that which is accessible to
awareness as evidenced by verbal report.” On this basis, I would
argue for the opposite presumption to theirs. First, it is clear
that while the processing capacity of the brain is vast, the
thought of which we appear to be aware or able to report is
very limited. Second, and despite the authors’ attempt to discredit
it, there is much evidence that our self-knowledge is poor and our
verbal reports of our mental processes most unreliable. Finally, it
borders in the mystical (or at least Cartesian dualism) to think of
consciousness as some kind of “mind stuff” that has powers of causation. All of our conscious experiences are a product of the brain,
because there is nothing else they could be. A conscious decision
is one of which we become (at some point) conscious, nothing
more nor less (see Evans 2010, Ch. 7).
Not only do the authors believe they have discredited the idea
of unconscious thinking, but also that in the process that they have
successfully attacked dual-system and dual-process theories of
higher cognition. Such theories distinguish between two kinds
of processing: Type 1, which is fast, is automatic, and has high processing capacity, and Type 2, which is slow, is deliberative, and has
low processing capacity (also known as System 1 and 2; see, e.g.,
Evans 2007; 2008; Kahneman 2011; Stanovich 2011). It is true
that the distinction between conscious and nonconscious processing has been emphasised by some social psychologists (e.g.,
Wilson 2002), but it is emphatically not the foundation for contemporary dual-process theories of reasoning and decision
making. As a dual-process theorist, I have argued, in common
with others, that the conscious/unconscious decision cannot be
the basis for the dual-process distinction because it is too vague
and fails to define the key central properties of dual processing
(Evans & Stanovich 2013). I should also point out that in spite
of defending the validity of much of the research that N&S criticise here, I have in common with them critiqued unconscious
thinking theory and other strong assertions of the powers of intuition (see Evans 2010, Ch. 4). This is because dual-process theory
confines powers of reflective reasoning – and with it the ability to
deal with novel and difficult problems – to Type 2 processing.
The case for dual process is in fact based not on the conscious/
unconscious distinction but on the claim that there are two forms
of cognitive processing which have distinctive properties and
which reflect the operation of distinct cognitive and neural
systems. Most of these properties are merely typical correlates,
and few are defining features (Evans & Stanovich 2013). I agree
with Stanovich that Type 2 processing is distinguished both by
its cognitive resources (central working memory, correlation
with measures of cognitive capacity) and by its ability to engage
in cognitive decoupling and hypothetical thinking. The apparent
link of dual-process theory with consciousness comes only from
the fact that some of the items attended in working memory are
available to verbal report. But using broader definitions of consciousness, I have argued in detail that both Type 1 and Type 2
thinking have aspects that are conscious as well as unconscious
(Evans 2010, Ch. 7).
In conclusion, not only do I reject the authors’ presumption of
conscious decision making, which I believe to be shakily founded
on folk psychology, but I also contest their implication that the
conscious/unconscious distinction is the basis for contemporary
theories of dual processing in higher cognition.
Dismissing subliminal perception because of
its famous problems is classic “baby with the
bathwater”
doi:10.1017/S0140525X13000708
Matthew Finkbeiner and Max Coltheart
Department of Cognitive Science and ARC Centre of Excellence in Cognition
and Its Disorders, Macquarie University, Sydney, NSW 2109, Australia
matthew.finkbeiner@mq.edu.au
max.coltheart@mq.edu.au
www.maccs.mq.edu.au/~mfinkbei
www.maccs.mq.edu.au/~max/
Abstract: Newell & Shanks (N&S) appeal to well-known problems in
establishing subliminality to argue that there is little convincing evidence
that subliminally presented stimuli can affect decision making. We
discuss how recent studies have successfully addressed these well-known
problems and, in turn, have revealed clear evidence that subliminally
presented stimuli can affect decision making.
Newell and Shanks (N&S) argue that there is little convincing evidence that subliminally presented stimuli can affect decision
making, because of two artifacts that can influence such experiments: conservatism of response criteria and effects of task difficulty on motivation. Our view is that both artifacts can be avoided
and that recent studies that have avoided them have revealed clear
evidence that subliminally presented stimuli can affect decision
making.
Conservatism of the response criterion. N&S use the study by
Hassin et al. (2007) to illustrate the response bias problem. In
this particular study, subjects were asked to indicate if a masked
stimulus was an Israeli flag or a control stimulus (scrambled
flag). In such a “yes/no” task, subjects might adopt a conservative
decision criterion and respond “no” even on those occasions in
which they had a fleeting glimpse of the real flag. While this is a
real concern, it is easily addressed. One solution is to calculate a
bias-free measure (d′), which is a standard technique in signal
detection theory (Green & Swets 1966). Another solution is to
use a two-alternative forced-choice (2AFC) paradigm, where subjects are provided with both possible stimuli simultaneously and
their task is to indicate which one of the two had been presented
immediately before as the masked stimulus. The advantage of
using the 2AFC paradigm is that it (1) greatly discourages
response biases and (2) generally provides a more sensitive
measure of stimulus detection and/or discriminability (Macmillan
& Creelman 2004. There are now several reports of subliminal
perception in the literature that have used the 2AFC paradigm
to assess subjects’ awareness of the masked stimuli (cf. Dell’Acqua
& Grainger 1999; Finkbeiner 2011; Finkbeiner & Palermo 2009).
Thus, the response-conservatism artifact is not an intractable
problem, and so claims of subliminal perception affecting decision
making should not be dismissed simply because this is a wellknown problem.
Effect of task difficulty on motivation. A somewhat more difficult problem to address is the task-difficulty artifact (Pratte &
Rouder 2009). In the standard subliminal perception experiment,
subjects are first asked to classify the targets and, subsequently,
the masked stimuli. Evidence in favor of subliminal perception
depends upon (1) a reliable behavioral effect in the targetclassification task and (2) chance-level performance in the
prime-classification task. A concern with this two-task design is
that the prime-classification task is, by design, very difficult and,
hence, poor performance might be due to task difficulty, not subliminality of the primes. If so, then the prime-classification task
will underestimate subjects’ awareness of the prime stimuli.
This is a well-known problem in the subliminal perception literature that has become known as the “task-difficulty artifact” (Pratte
& Rouder 2009). But well-known problems are not necessarily
intractable problems. In fact, researchers have long known
about the task-difficulty artifact and have addressed it in various
ways. The most frequently employed solution is to interleave
long-duration primes, which are easier for subjects to classify,
into the prime-classification task. That serves to make the
prime-classification task easier overall and, hence, is thought to
encourage subjects to “keep trying” at an otherwise difficult
task (cf. Finkbeiner & Caramazza 2008; Finkbeiner et al. 2004;
Grainger et al. 2003; Naccache & Dehaene 2001).
Recently, we sought to eliminate the task-difficulty artifact by
bringing performance in the prime-classification task up to
ceiling levels (>90% accurate), even while maintaining subliminality (Finkbeiner 2011). We did this by presenting the masked
prime stimuli (the words dog or may) in red or green ink. Immediately following presentation of the masked stimulus, we presented
subjects with the four possible prime stimuli (formed by crossing
the two prime words with the two ink colors) and asked them to
point to the correct one. Using this task, we found that subjects
were very good at color identification (>90%), but were at
chance at word identification (~50%). Thus, the task-difficulty
artifact is not an intractable problem, and so, again, claims of subliminal perception should not be dismissed simply because this is a
well-known problem.
A third problem: null sensitivity. A far more pernicious problem,
and one that N&S do not mention, is the null-sensitivity problem.
Here the problem has to do with a lack of sufficient power for the
standard null hypothesis significance test (NHST) approach to
accurately resolve performance that is only slightly above
chance levels. It may seem that the solution would be to increase
the power of the experiment, but this is ultimately not feasible.
For example, with a sample size of 21 subjects with a mean true
performance of 52%, one would need approximately 570 primeclassification trials to bring the probability of wrongly accepting
the null hypothesis down below 5%. With a true performance of
51%, approximately 2,300 trials would be needed. This is the
null-sensitivity problem, and trying to resolve it through the
addition of more and more trials is not practical for most researchers or their subjects.
Fortunately, the standard NHST approach is not the only way
to test for chance-level performance. In a series of recent articles,
Rouder et al. (2007) and Morey et al. (2008; 2009) have introduced a hierarchical model within the Bayesian framework that
offers researchers a way to resolve the null-sensitivity problem.
They have termed this the mass-at-chance (MAC) model. A
virtue of MAC is that it penalizes underpowered designs,
thereby mitigating the null-sensitivity problem. Small sample
sizes yield highly variable posterior estimates of subjects’ latent
abilities, which makes it more difficult to claim subliminality
(Rouder et al. 2007). In one recent study (Finkbeiner 2011), we
used the one-parameter MAC model (Morey et al. 2008) and
found that, with 9:1 odds in favor of subliminality, our experiment
yielded 16:1 odds in favor of priming. Thus, even the most difficult
problem in the subliminal perception literature, the null-sensitivity problem, is not without solutions.
Conclusion. We conclude, contra N&S, that recent studies have
provided clear evidence that subliminally presented stimuli can
affect decision making.
27
How necessary is the unconscious as a
predictive, explanatory, or prescriptive
construct?
doi:10.1017/S0140525X1300071X
Claudia González-Vallejo,a Thomas R. Stewart,b
G. Daniel Lassiter,a Justin M. Weindhardta
a
Psychology Department, Ohio University, Athens, OH 45701; bCenter for
Policy Research and Department of Public Administration and Policy,
Rockefeller College of Public Affairs and Policy, State University of New York
at Albany, Albany, NY 12222.
gonzalez@ohio.edu
t.stewart@albany.edu
danlassiter@mac.com
jw225207@gmail.com
http://www.ohioupsychology.com/
http://www.albany.edu/cpr/
Abstract: We elucidate the epistemological futility of using concepts such
as unconscious thinking in research. Focusing on Newell & Shanks’
(N&S’s) use of the lens model as a framework, we clarify issues with
regard to unconscious-thought theory (UTT) and self-insight studies.
We examine these key points: Brunswikian psychology is absent in UTT;
research on self-insight did not emerge to explore the unconscious; the
accuracy of judgments does not necessitate the unconscious; and the
prescriptive claim of UTT is unfounded.
Daryl Bem (1972) foresaw a slippery slope in resorting to unconscious processes as explanatory variables. The point was not to
deny that mental activity could occur outside of awareness, but to
warn researchers about abandoning sound epistemological practices
when explaining phenomena. We add to this a concern about deriving prescriptions from theories that have not been extensively
tested; from studies that have not been widely replicated; and
from the usage of terms, such as unconscious, that have not been
consistently defined. In this light, we commend Newell & Shanks
(N&S) for their critical review of unconscious influences on decision
making and agree with their conclusions. However, we identify areas
needing clarification following their use of the lens model (Brunswik
1952; Hammond & Stewart 2001) as an interpretive framework.
Greenwald (1992, p. 775) concluded that unconscious cognition
occurs rapidly and is “severely limited in its analytic capability.” In
contrast, unconscious-thought theory (UTT; Dijksterhuis & Nordgren 2006) assumes a deliberative and temporally extended unconscious that can sift through vast amounts of information to arrive
at optimal decisions. UTT experiments, as described by N&S, use
a unique multi-attribute evaluation task that presents participants
with cue values, sequentially and randomly. We agree with N&S
that the evidence supporting the benefits of unconscious thinking
is weak. The application of the lens model to research based on
UTT, however, is not self-evident. First, N&S identify points
within the lens model where lack of awareness could take place;
however, lack of awareness and UTT’s unconscious processing do
not equate. Second, it is worth adding that UTT is not Brunswikian
in spirit. Representative design is absent; most UTT studies use a
small set of objects with attributes and their values selected by
the experimenters. The cue values are presented in random
order across cases in UTT studies, whereas in most judgment situations the unit of information acquisition is organized first by case/
object. And in terms of accuracy, UTT uses agreement between
judgments and the experimentally defined “best option” rather
than by correspondence of judgments with agreed-upon environmental criteria. N&S note that within the lens model, a source of
lack of awareness may occur at the weighting of cues stage. We
add that this relates to UTT’s Principle 4, which claims that unconscious weights the relative importance of attributes in an efficient
manner; but the evidence supporting this principle is missing (see
González-Vallejo et al. 2008).
From a historical perspective, we note that multiple-cue judgment research did not directly attempt to study unconscious processes, even when considering the topic of self-insight. Hammond
(1955) advocated for the use of a quantitative technique to make
28
the judgment process explicit, in the sense of revealing which cues
were most influential. The impetus behind this was not rooted in discovering unconscious processes, but simply in the realization that
judgments had not been systematically studied and were impacting
lives in important domains (e.g., clinical judgments). Because most
psychological and physical processes are not easy to verbalize,
modern psychological research shifted from relying on verbal
reports to using psychometric techniques, and this ensued in judgment research as well. The focus on self-insight evolved from contrasting statistically estimated cue weights with the verbal
descriptions of what was important. As N&S show, that agreement
is variable, but the goal of the approach was not about understanding
unconscious processes but rather about employing statistics to help
individuals communicate the basis for their judgments (Hammond
& Adelman 1976). More generally, mathematical models of cognition are ubiquitous and use many function forms. The view that individuals may be able to verbalize model parameters, thus showing
self-insight, is an interesting but not very useful proposition.
Indeed, even if we think mathematical models are about the unconscious, a notion like self-awareness would be unnecessary. We do
agree with N&S that the validities of measures of self-insight are
questionable, but we add the caveat that both subjective assessments
and statistical estimates of parameters depend on a model, so neither
has priority over the process they are measuring.
From another perspective, lens model research has yielded a
rich body of work (Karelaia & Hogarth 2008). The main results
are (a) linear models capture similar and relatively high proportions of variance in environmental outcomes and in human
judgments, and (b) judges reach high levels of accuracy when predicting criteria in many domains. Factors that affect accuracy can
be safely classified as task/environmental characteristics (see also
Stewart et al. 1997). Therefore, on logical grounds, there is little
need to resort to unconscious thinking as an explanatory variable
of judgments, or as a mechanism for improving accuracy.
Because of Hammond’s central role in lens model research, we
feel his views on intuition must be mentioned. Cognitive continuum
theory (CCT; Hammond 1986; 1996; Hammond et al. 1997) states
that both tasks and cognitive processes are located on an intuitiveto-analytic continuum. According to Hammond, most judgment is
“quasi-rational,” involving a combination of intuition and analysis
(Hammond 1996), thus contrasting with dual-process conceptions
(Epstein 1994; 2003; Kahneman 2011) and with UTT’s first principle
of two modes of thought. (We refer the reader to our analysis of this
principle in González-Vallejo et al. 2008.) In short, Hammond’s
notion of quasi-rationality is similar to modern conceptions of cognition. In particular, with the emergence of parallel processing models
(e.g., Rumelhart et al. 1986) and more generally connectionist models
(Phaf & Wolters 1997), psychologists favor the view that responses
reflect a mixture of unconscious and conscious contributions.
We end by revisiting the prescription that complex decisions
should be left to unconscious thinking. Many years of research
converge on the conclusion that selecting important predictors
is best done by experts, but the combination of cues is best left
to a statistical tool (Bishop & Trout 2005; Dawes 1979).
Imagine a psychiatrist judging the likelihood that a patient will
commit suicide; the prescription that she or he should let the
unconscious decide is not only wrong, it is also unethical.
Do implicit evaluations reflect unconscious
attitudes?
doi:10.1017/S0140525X13000721
Adam Hahn and Bertram Gawronski
Department of Psychology, The University of Western Ontario, Social Science
Centre, London, Ontario N6A 5C2, Canada.
adam.hahn@uwo.ca
bgawrons@uwo.ca
http://publish.uwo.ca/~bgawrons/people.htm
Abstract: We extend Newell & Shanks’ (N&S’s) arguments to the
question of whether implicit evaluations reflect unconscious attitudes.
We argue that correspondence to explicit evaluations fails to meet the
criteria of relevance and sensitivity. When awareness is measured
adequately and in line with N&S’s criteria, there is compelling evidence
that people are consciously aware of their implicit evaluations.
Newell & Shanks (N&S) call for a more rigorous study of awareness using reliable, relevant, and sensitive measures that are administered when the relevant mental operation is taking place. In the
current commentary, we extend N&S’s arguments to implicit
evaluations, which can be conceptualized as evaluative responses
captured by performance-based measures, such as the Implicit
Association Test (Greenwald et al. 1998), evaluative priming
(Fazio et al. 1995), and various response interference tasks (for
a review, see Gawronski et al. 2011). Implicit evaluations are
widely assumed to reflect unconscious attitudes that are held
outside of awareness (for a review, see Gawronski et al. 2006).
Drawing on N&S’s conceptual framework, we argue that lack of
awareness in the domain of implicit evaluations is inferred from
incomplete evidence that does not warrant the conclusion of
unawareness.
Characterizations of implicit evaluations as reflecting unconscious attitudes are based on the finding that implicit evaluations
typically show rather low correspondence to self-reported explicit
evaluations of the same target object (for meta-analyses, see
Cameron et al. 2012; Hofmann et al. 2005). However, the conclusion that dissociations between implicit and explicit evaluations
indicate unawareness of the former violates N&S’s criteria of relevance and sensitivity.
Research and theorizing suggest that encountering an attitude
object spontaneously activates evaluative associations in memory
(De Houwer 2009; Ferguson & Zayas 2009). Performancebased measures are assumed to capture these associations regardless of whether the person considers them valid. When a person is
asked to report an explicit evaluation, activated associations are
assessed for their (subjective) validity by propositional processes
(Gawronski & Bodenhausen 2006; 2011). To the extent that the
evaluation implied by activated associations is consistent with
other salient propositions, it is typically regarded as valid and
reported on measures of explicit evaluations. However, if the
evaluation implied by activated associations is inconsistent with
other salient propositions, consistency has to be restored before
an explicit evaluation can be reported (Festinger 1957). In such
cases, implicit and explicit evaluations often diverge, such that
implicit evaluations reflect activated associations regardless of
their perceived validity, whereas explicit evaluations reflect activated associations that are regarded as valid (e.g., Gawronski &
LeBel 2008; Gawronski & Strack 2004; Gawronski et al. 2008).
From this perspective, an explicit evaluation is not a measure of
a person’s awareness of his or her implicit evaluation. Instead, it
reflects the role of propositional processes in assessing the subjective validity of activated associations.
The inference that implicit evaluations reflect unconscious attitudes because they show low correspondence to explicit evaluations thus violates N&S’s criteria of relevance and sensitivity.
Low correspondence between implicit and explicit evaluations is
not relevant for awareness of implicit evaluations, because explicit
evaluations may differ from implicit evaluations for reasons other
than lack of awareness. Moreover, low correspondence is not sensitive, because measures of explicit evaluation do not ask participants to merely report their evaluative associations, but to
report the evaluative associations that they regard as valid.
To overcome these limitations, we have recently started a
research project in which we asked participants to predict their
implicit evaluations of multiple target groups before completing
corresponding measures of implicit evaluation (Hahn et al., in
press). We argue that predictions of implicit evaluations are
both more relevant and more sensitive for inferences about awareness than correspondence to explicit evaluations. Predictions are
more relevant, because they rule out cognitive inconsistency as
a potential cause of diverging explicit evaluations. Moreover, predictions are more sensitive, because they directly capture participants’ ability to report their implicit evaluations (e.g., “If we ran a
computerized test, what would it show?”) rather than evaluations
that they perceive as valid (e.g., “How much do you agree with the
statement that group X is likeable?”). Our studies consistently
showed that participants were highly successful in predicting
their implicit evaluations, even in cases where they reported distinct explicit evaluations. In line with previous findings (e.g.,
Blair 2001; Hofmann et al. 2005; Nosek 2005), implicit and explicit evaluations revealed correlations around 0.20. In contrast, participants’ predictions showed mean correlations with implicit
evaluations higher than 0.50 and median correlations of around
0.65.
Our research also led to some additional discoveries that highlight the benefits of studying awareness more rigorously. For
example, high levels of accuracy in predicting implicit evaluations
were found primarily when accuracy was determined within subjects (i.e., rank order of evaluations of different target groups for
each participant). However, when accuracy was determined
between subjects (i.e., rank order of evaluations of the same
target group across participants), prediction accuracy was lower.
In other words, although participants were able to predict their
implicit evaluation of a given target group vis-à-vis other target
groups (within-subjects analysis), their predictions were less accurate for identifying their implicit evaluations of a given target
group vis-à-vis other participants (between-subjects analysis).
Interestingly, participants also predicted lower levels of implicit
evaluative bias against outgroups for themselves than for other
participants. These findings suggest that, although people are
aware of the evaluative quality of their implicit evaluations, they
may not be aware of how their implicit evaluations compare to
those of other people. That is, people seem to be aware of some
aspects of their implicit evaluations (e.g., the fact that they hold
more biased implicit evaluations against some groups than
others) but not others (e.g., whether these biases are stronger
than those of other people), and studying these two “kinds” of
awareness requires different methods (i.e., within-subjects analysis vs. between-subjects analysis). Thus, rather than treating
awareness as an all-or-none issue, our findings highlight the
importance of more fine-grained analyses when studying conscious awareness.
In sum, we agree with N&S’s concern that unawareness of
psychological processes is often inferred from insufficient evidence. We argue that inferences of unawareness from dissociations between implicit and explicit evaluations violate the
criteria of relevance and sensitivity. Research using more adequate measures indicates that implicit evaluations can be predicted with accuracy, suggesting that implicit evaluations do not
reflect unconscious attitudes.
But what if the default is defaulting?
doi:10.1017/S0140525X13000733
Ran R. Hassina,b and Maxim Milyavskya
a
Psychology Department, The Hebrew University, 91905 Jerusalem, Israel;
The Center for the Study of Rationality, The Hebrew University, 91904
Jerusalem, Israel.
ran.hassin@huji.ac.il
maxim.milyavsky@mail.huji.ac.il
http://labconscious.huji.ac.il
psychology.huji.ac.il/en/?cmd=Graduate.27&letter=m&act=read&id=60
b
Abstract: We critically consider the default view of consciousness and
decision making, and we explore the implications of this view to the
authors’ argument. We therefore call for rigorous collection of data
regarding the role of consciousness in decisions. We also propose that
29
the section on subliminal effects significantly underestimates existing data
and succinctly review relevant findings.
There are many things to like about the target article, and we
thank Newell & Shanks (N&S) for their efforts: They will definitely result in a better science, which is what we all want, after
all. Our gratitude, however, should not be mistaken for agreement: We widely disagree. For lack of space we will focus on
two issues, one theoretical and one empirical.
Imagine the Snamuh, a species with brains and cognition just
like ours. Unlike us, however, the Snamuhs are very proud of
their unconscious: They believe that it is what sets them apart
from the rest of the animal kingdom. Consciousness, they think,
does not play a significant role in their lives.
Recently, papers in Snamuh-land’s scientific journals suggested
that consciousness matters. Curious about these developments,
two of Snamuhs’ best cognitive scientists examined the evidence
in light of strict criteria. Among them are the relevance criterion
(“assessments should target only information relevant to the behavior,” N&S Table 1), and the sensitivity criterion (“assessment
should be made under optimal retrieval conditions,” N&S Table 1).
The scientists were not convinced by the data. After all, conscious information might be “contaminated” (Wilson & Brekke
1994) by non-conscious processes, rendering the task of
meeting the relevance criterion difficult. Similarly, assessing sensitivity of conscious measures is not a trivial task, threatening the
sensitivity criterion. Accordingly, they concluded “conscious influences – if they exist at all – have limited and narrow effects.”
The moral is straightforward: N&S’s default is that decisions are
conscious. This is their H0. The evidence to the contrary, they
suggest, is not convincing enough (we disagree, of course).
Hence, H0 is not rejected and consciousness it is. Yet, the
Snamuhs used the same scientific logic. But with the opposite
H0, their conclusion was diametrically opposed. So which one is it?
We propose that the default view is less likely than it might
seem. First, our consciousness is notoriously limited in its capacity
(e.g., Baddeley & Hitch 1974; Kahneman 1973). Given the sheer
volume of problem solving, goal pursuits, self-regulation, and the
like, that we engage with, it seems reasonable to suspect that we
benefit from non-conscious progress too. Second, our best theories (to date) suggest that the type of consciousness that we
have is relatively new in the evolutionary sense. It seems unlikely
that basic functions such as decision making necessitate this “new”
invention (see Hassin 2013; Hassin & Sklar, in press; Sklar et al.
2012).
Given this assessment, it will be fruitful to conduct research
with the Snamuhs’ H0. We urge scientists who argue that consciousness has a role in a cognitive function F (whatever F
might be) to adopt strict criteria and measurements, similar to
those adopted by researchers of the unconscious. For example,
have them measure all of the relevant unconscious effects and
contents and devise measures that are unaffected by irrelevant
unconscious data. To the best of our knowledge, there is little discussion of how one should go about addressing these issues (but
see Jacoby 1992) – nothing that is even reminiscent of the
debates that allowed the science of the unconscious to improve
so much over the decades (with the kind help of the authors of
the target article).
In the realm of high-level cognition, a role of consciousness is
usually intuitively assumed or inferred from (what N&S call)
weak evidence for unconscious processes. We would very much
like to see positive, strong evidence of the kind described above.
We believe there must be. Until this kind of research blooms,
however, we cannot really estimate to what extent conscious processes affect decisions.
Our second point has to do with the literature coverage. For
lack of space, we will focus on the subliminal priming section.
We are happy to have been reviewed in this section; we are very
proud of this work (see also Carter et al. 2011). But there is a lot of
evidence that is not reviewed there. For example, thirsty
30
participants who had been subliminally primed with thirstrelated words drank more than non-primed participants
(Strahan et al. 2002; see also Veltkamp et al. 2011). Extending
these findings, Karremans et al. (2006) showed effects of subliminal brand names (see also Bermeitinger et al. 2009; Verwijmeren
et al. 2011). Subliminal primes also affect choices in “free choice”
paradigms (Kiesel et al. 2006; Klapp & Haas 2005; Schlaghecken
& Eimer 2004), and we recently demonstrated effects of subliminal priming on choice, in a task that used both objective and subjective measures on a trial-by-trial basis (Milyavsky et al. 2012).
Related research examined processes that are likely to affect
decisions and has documented subliminal/implicit effects on
executive functions and working memory (Hassin et al. 2009;
Lau & Passingham 2007; van Gaal et al. 2008), affect (e.g.,
Monahan et al. 2000), numbers and arithmetic (e.g., Opstal
et al. 2011; Sklar et al. 2012), incentives (e.g., Bijleveld et al.
2009), goal pursuit (e.g., Ferguson 2008), information integration
(Mudrik et al. 2011), fear (Raio et al. 2012) and fear of death (e.g.,
Arndt et al. 1997), anchoring (e.g., Mussweiler & Englich 2005),
self-evaluation (Mussweiler et al. 2004), and attitudes (e.g.,
Loersch et al. 2011), to name, really, just a few (see Sklar et al.
2012 for more).
The authors raise a few general worries about these kinds of
studies. Yet, the debate about measuring awareness has been
with us for decades. Yes, there are justified concerns, but also,
yes, there are good answers, which are met by counterarguments,
and replies, and the development of new measures, and so on and
so forth: scientific dialectics at its best. The question of subliminal
semantic processing, for example, has been with us for decades
too, and recent reviews suggest that it is possible (Kouider &
Dehaene 2007; Van den Bussche et al. 2009).
To conclude, we believe that the data for subliminal effects on
various aspects of decisions is much larger, and much stronger,
than the data reviewed here. We therefore believe that the conclusions of this section do not adequately represent the state of
affairs in this blooming area of the cognitive sciences.
Context, as well as inputs, shape decisions,
but are people aware of it?
doi:10.1017/S0140525X13000745
Erik G. Helzera and David Dunningb
a
Department of Psychology, Wake Forest University, Winston-Salem, NC
27109; bDepartment of Psychology, Cornell University, Ithaca, NY 14853.
helzereg@wfu.edu
dad6@cornell.edu
Abstract: Even if people are experts at understanding how various input
cues landed them at a particular decision (something the authors refer to
as cue utilization), they may still fail to appreciate how context influences
the weight given to those input variables. We review evidence suggesting
that people are unaware of contextual influences on their decisions.
Newell & Shanks (N&S) raise important questions about whether
people possess self-insight into the factors that shape their
decisions. Our critique is motivated not by the evidence that
was reviewed in the article, but by a large and essential body of
literature that was not – literature suggesting that people are
unaware of how contextual factors influence their use of information and the eventual decisions they reach.
Suppose that for any decision two broad types of variables come
into play: input variables and context variables. Input variables are
informational variables that people use in determining what to
think or how to act. For example, someone decides to vote for a
candidate based on his or her perceptions of that candidate’s political leanings, competence, and likability. Context variables, by
contrast, are “background” variables that affect the way people
think about a particular decision or weight relevant pieces of
information. In one context, a voter might weight the candidate’s
competence over likeability; in another context, the same voter
might weight the candidate’s likeability over competence.
On our reading, N&S focus their critique of awareness on input
variables, making the claim that people have insight into the
various inputs that shaped their responses (e.g., Harries et al.
2000). Even if we stipulate this as true, research not reviewed
by the authors has shown that people remain unaware of how
their use of those inputs is influenced by the context in which
the decision was made. People may know which input variables
shaped their decision, but they still may lack insight into how
context nudged them to rely on those variables.
Imagine someone deciding on Kahneman and Tverksy’s classic
Asian Disease problem. They have a chance to save the lives of
600 people by choosing which of two drugs to recommend. In
one version of this problem, they learn that Drug A will save 200
people with certainty and Drug B has a 1-in-3 chance of saving
everyone (otherwise, no one is saved). Most people in this situation
recommend Drug A. Let’s assume they know why: They value the
certainty of saving 200 over the uncertainty of Drug B. However, in
another version of this problem, the same question is framed in
terms of losses, such that Drug A will result in the death of 400
people with certainty and Drug B carries the 2-in-3 chance that
everyone will die (with a 1-in-3 chance that no one dies). Asked
with this framing, people now prefer Drug B, and again can articulate why – it’s better to take a chance on saving everyone than kill
400 for sure. Thus, depending on the question asked, people
arrive at two very different answers, and they can describe accurately why they decided as they did. To our minds, though, the
interesting self-insight question still remains: Do people know
how the context of the decision affected them? Do they know
that their use of the information and eventual decision was
shaped in large part by the question framing?
Emerging evidence reveals that people tend to have inadequate
insight into the impact of significant context variables on their
decisions. In this work, people are asked how their preferences
or decisions would change if the circumstances surrounding
their decisions were different. For example, they are asked how
likely they would be to help another person if placed in a group
that passively did nothing versus in a room alone with the other
person—the classic bystander intervention effect. People claim
that being in a group would do nothing to their tendency to
help, but in reality people are much less likely to help in those circumstances (Balcetis & Dunning 2013; see Epley & Dunning
2000, for conceptually similar results).
Similarly, people fail to understand how emotion changes their
preferences (Van Boven et al. 2013). Many people will say, when
asked hypothetically, that they would be willing to dance in front
of a classroom full of peers for $5; a substantially smaller number
actually accept this opportunity when faced with the real choice.
What may explain this difference between hypothetical and real
decisions is that people fail to understand how the context of
the decision – real versus hypothetical – changes their decisionmaking process. In the former case, people weight the $5 compensation over their fear of social evaluation. In the latter, that
weighting flips such that the fear of social evaluation trumps the
lure of compensation (Van Boven et al. 2012).
A range of contextual variables known by social psychologists to
affect decision making appear to go largely undetected by decision
makers themselves. People do not recognize how sensitive their
decisions are to social norms (Nolan et al. 2008). They believe
that they are more immune to conformity pressures than they
really are (Pronin et al. 2007). And they similarly overestimate
their resistance to media influence (Davidson 1983; Douglas &
Sutton 2008; 2010; Perloff 1993; 1999).
Or, consider the following: We (Helzer & Dunning 2013) have
asked people to make a decision between X and Y, with knowledge
that most of their peers think X. The opinions of their peers have
an effect on the participants’ ultimate decision, but do the participants know this? After they reach a decision, we ask them what
their decision would have been if, instead, their peers had
favored Y. As in the studies cited above, people consistently
underappreciate the impact this shift will have on their decisions,
thinking that their decisions are insensitive to this changing
context, when, in fact, they are quite sensitive.
Thus, even if people are experts at understanding how various
input cues landed them at a particular decision (something N&S
refer to as cue utilization), we assert on the basis of relevant
empirical findings that they fail to appreciate how context influences the weight given to various decisional inputs. This leaves
them with some recognition of how they arrived at their ultimate
decision, but not necessarily why the decision unfolded as it did.
Automatic processes, emotions, and the
causal field
doi:10.1017/S0140525X13000757
Robin M. Hogarth
Universitat Pompeu Fabra, Department of Economics & Business, 08005
Barcelona, Spain.
robin.hogarth@upf.edu
Abstract: Newell & Shanks (N&S) provide a welcome examination of
many claims about unconscious influences on decision making. I
emphasize two issues that they do not consider fully: the roles of
automatic processes and emotions. I further raise an important
conceptual problem in assigning causes to potential unconscious
influences. Which “causal field” is relevant: that of the investigator or
the experimental participants?
As noted by Newell & Shanks (N&S), recent years have seen
numerous authors extolling the virtues of unconscious processes
in decision making. From some accounts, it would appear that
we would all do better if we were just to “let our unconscious
do the thinking.”
N&S question several claims for the influence of unconscious
influences, and their findings give pause for solid conscious
thought. What precisely are the influences of unconscious processes in decision making? How can these be reliably detected?
These are difficult questions, and my goal here is to add some
elements to the debate.
My first point is to express surprise that N&S do not explore more
the topic of automatic processes and the extent to which these do or
do not involve unconscious influences. It is clear that in learning
physical skills (imagine riding a bicycle), people go through a
process whereby, at the outset, they are conscious of their movements, but once skilled, they can control what they do without
paying much, if any, conscious attention. It is difficult to prove or disprove the role of unconscious influences in these kinds of situations,
but an analogy can clearly be made with judgments or decisions. Is
the use of an automatic process conscious or, on the contrary, an
unconscious reaction to a process that has already been initiated?
One interesting phenomenon lies in decisions that have been
taken automatically but for which there is no longer a conscious
memory. (Does this mean they were subject to unconscious influences?) For example, like many other people I am supposed to
swallow a pill every morning. Occasionally, I cannot remember
if I have done this. Should I then deliberately take – perhaps –
another pill? Fortunately, my pill container indicates days of the
week. Thus, when I wonder whether I have taken my pill, I
look to see if the pill for that day is still there. If it is, I didn’t
take my pill; if it isn’t, I did. Parenthetically, this example does
not satisfy N&S’s criteria for detecting an unconscious influence
because, if I had been asked just after taking the pill whether or
not I had done so, my answer would undoubtedly be that I
had – it is the delay that appears to erase this automatic event
from memory.
31
A second topic that N&S do not highlight is the role of emotions
in decision making and whether this involves unconscious influences. There is a considerable literature that illustrates how
emotional states affect judgments of risk and even risky decision
making (Andrade & Ariely 2009; Slovic & Peters 2006). For
example, in one research program, my colleagues and I used
experience sampling to collect judgments of mood and emotion
as well as assessments of risk (Hogarth et al. 2011). We found
that emotional states explained variability in risk judgments over
and above rational factors of probabilities and magnitudes of
potential losses. We did not attempt to determine whether participants were aware that their emotional state was impacting their
risk judgments. However, from an N&S perspective, future
studies could clearly do this. My hypothesis is that people are
not always aware of how emotions influence their decisions in
the same way that these might be affected by, for example, relative
states of hunger (Danziger et al. 2011).
A third point deals with a difficulty in interpreting the differences that researchers and participants in experiments have concerning whether a variable has had a causal influence on a
decision. An example given in a seminar some 30 years ago by
Richard Nisbett illustrates the point. (Incidentally, although I
like and remember the scenario, I do not recall the specific
point that Nisbett was illustrating!)
Imagine that a social psychologist is conducting a study on the
influence of lighting in restaurants on romantic attachment.
Couples are recruited for blind dates involving a meal at a restaurant, and there are two experimental conditions to which couples
are randomly assigned. In one, the restaurant is fully illuminated;
in the other, the lights have been dimmed. The dependent variable is the proportion of couples who decide to meet again after
the meal. Now imagine that this variable is significantly greater
for couples in the dimmed lighting condition. What does this
mean? For the social psychologist, the inference is that dimmed
lighting fosters romantic attachment. After all, this was the variable that was manipulated experimentally, and there was an effect.
Now imagine that you ask the couples whether the lighting in
the restaurant influenced their decisions to meet again. Almost
certainly, they would deny that this had played any role.
So who is “correct” – the social psychologist or the couples? It is
possible to make an argument that both are correct. What differs
between the two is the definition of the causal background – or
“field” – against which the causal inference is made (Einhorn &
Hogarth 1986; Mackie 1965; 1974). For the social psychologist,
the causal field involves both experimental groups (with and
without dimmed lighting), and the difference in the levels of lighting
is a “difference-in-the-field” and thus a potential causal factor. The
causal fields of the experimental participants, however, contain no
such difference. The experience for each couple consists entirely
of dimmed or full lighting, and they never experience the differences
between the two conditions. For the couples, therefore, there is no
way that they can assign cause to the level of lighting. For each
couple, lighting is a constant and thus not causally relevant.
Advocates of influences of unconscious effects on decisions
would undoubtedly agree with the social psychologist. However,
this conclusion only holds at one level of analysis (i.e., causal
field). In general, we should be clear at which levels we wish to
draw conclusions.
Is the unconscious, if it exists, a superior
decision maker?
doi:10.1017/S0140525X13000769
Hilde M. Huizenga,a,b Anna C. K. van Duijvenvoorde,a,c
Don van Ravenzwaaij,d Ruud Wetzels,e,f and
Brenda R. J. Jansena,b
32
a
Department of Psychology, University of Amsterdam, 1018XA Amsterdam,
The Netherlands; bCognitive Science Center Amsterdam, University of
Amsterdam, 1018XA Amsterdam, The Netherlands; cFaculty of Social
Sciences, Leiden University, 2333AK Leiden, The Netherlands; dSchool of
Psychology, The University of Newcastle, Callaghan NSW 2308, Australia;
e
Informatics Institute, University of Amsterdam, 1098XH Amsterdam, The
Netherlands; fSpinoza Centre for Neuroimaging, 1018WS Amsterdam, The
Netherlands
h.m.huizenga@uva.nl
A.C.K.vanDuijvenvoorde@uva.nl
don.vanravenzwaaij@newcastle.edu.au wetzels.ruud@gmail.com
B.R.J.Jansen@uva.nl
http://home.medewerker.uva.nl/h.m.huizenga/
http://home.medewerker.uva.nl/a.c.k.vanduijvenvoorde
http://www.donvanravenzwaaij.com
www.ruudwetzels.com
http://home.medewerker.uva.nl/b.r.j.jansen/
Abstract: Newell & Shanks (N&S) show that there is no convincing
evidence that processes assumed to be unconscious and superior are
indeed unconscious. We take their argument one step further by
showing that there is also no convincing evidence that these processes
are superior. We review alternative paradigms that may provide more
convincing tests of the superiority of (presumed) unconscious processes.
In their short abstract, Newell & Shanks (N&S) state: “Recommendations to ‘stop thinking’ and rely on ‘gut instincts’
reflect widely held beliefs that our decisions can be influenced
by unconscious processes.” (N&S) predominantly focus on the
second part of this phrase and show that there is no convincing
evidence these processes are indeed unconscious. We take their
argument one step further by addressing the first part of their
phrase. That is, we discuss whether there is evidence that these
decisions, presumably based on the unconscious, are superior to
those based on thinking.
To determine whether presumed unconscious decisions are
superior to conscious ones, we first need to define what constitutes a good decision. To this end, we use the distinction between
compensatory and non-compensatory decisions. In compensatory
decisions, options are compared on their probability weighted
sum of all attributes, in which probability and attributes are evaluated objectively or subjectively (e.g., Tversky & Kahneman
1992). Non-compensatory decisions, however, are not based on
a weighted sum of all attributes. For example, in Dawes’ strategy
(e.g., Bröder & Schiffer 2003a) decisions are based on the number
of positive attributes, and in a lexicographic strategy (e.g., Tversky
& Slovic 1988), decisions are based on a sequential comparison of
attributes, in which a decision is made if options differ sufficiently
on an attribute under consideration. There seems to be general
consensus that compensatory decisions are superior to non-compensatory ones, as all attributes are taken into account (yet see
Payne et al. 1988 for an interesting counterexample).
Dual-process theories (e.g., Kahneman 2011; Stanovich & West
2000) posit that non-deliberative processes often yield non-compensatory decisions, whereas deliberative processes generate
compensatory ones. This hypothesis is supported by evidence
showing that non-compensatory decisions are common in case
of mental overload, which is assumed to hinder full reliance on
the deliberative system (Bröder & Schiffer 2003b; cf. Pohl et al.
2013). Interestingly, proponents of unconscious decision making
argue the opposite: They state that the non-deliberative system
facilitates compensatory decisions, whereas the deliberative
system facilitates non-compensatory ones (e.g., Dijksterhuis
et al. 2006b). In the following we determine whether the two
decision-making paradigms discussed by (N&S), the Iowa Gambling Task (IGT) and the paradigm of Unconscious Thought
Theory, the Unconscious Thought Paradigm (UTP), offer the
possibility to test this alternative claim.
In the IGT, decision makers presumably relying on unconscious
processes would opt for the two options (C & D) with the highest
expected value (Bechara et al. 1994); that is, they would use an
objective compensatory strategy. However, IGT studies often do
not allow for a test of this claim, as choices for specific options
are generally not reported. The few studies that did include an
analysis of specific options support a different conclusion (e.g.,
Duijvenvoorde et al. 2010; Horstmann et al. 2012; Lin et al.
2012). That is, decision makers generally prefer options with
low probability of losses (B & D), and some, but certainly not
all, decision makers gradually develop a preference of D (low
losses, low gains) over B (high losses, high gains) (Huizenga
et al. 2007). It is not very likely that the latter decision makers
adopted a compensatory strategy, as they did not prefer both
optimal options (C & D). It is more likely that these decision
makers adopted a non-compensatory lexicographic strategy, in
which they first considered probability of losses and then losses
themselves. These findings show that in the IGT, participants
using non-compensatory and compensatory strategies may arrive
at similar decisions. We therefore conclude that the IGT is not
suitable to differentiate decision strategies.
According to Unconscious-Thought Theory (Dijksterhuis et al.
2006b), decision makers who presumably rely on unconscious
processes would prefer the option with the highest compensatory
value over all attributes. However, using importance ratings of
attributes, it was shown that the compensatory strategy, Dawes’s
strategy, and a lexicographic strategy all converged on the same
choice (Huizenga et al. 2012). Therefore we conclude that the
UTP also does not allow for a differentiation of decision strategies.
The evidence reviewed above suggests that the IGT and UTP
are not suited to identify decision strategies and therefore are
not suited to test whether presumably unconscious decision processes facilitate compensatory decision making. To test this
claim of compensatory decision making, the field requires new
paradigms that allow assessment of decision strategies, namely,
paradigms in which compensatory and non-compensatory strategies result in different decisions. Fortunately, both within, as
well as outside, the IGT and UTP literature, paradigms are
being developed that suit this purpose. In the IGT-related field
there exists a paradigm that allows a further study of lexicographic
versus compensatory strategies (Lin et al. 2009). In the UTP literature, there are paradigms to delineate Dawes and compensatory strategies (Payne et al. 2008; Usher et al. 2011) and to
delineate lexicographic and compensatory strategies (Huizenga
et al. 2012). Outside these fields it has been shown that processtracing techniques (Bröder & Schiffer 2003a; Payne et al. 1988)
provide valuable tools to study decision strategies. In addition, it
has been shown that modern statistical techniques like mixture
analyses offer the possibility to differentiate decision strategies
(Duijvenvoorde et al. 2010; Jansen et al. 2012).
To conclude, the evidence in favor of the superiority of unconscious decisions is not convincing, as paradigms like the IGT and
UTP do not allow for an assessment of decision strategies.
However, there do exist new paradigms, experimental
approaches, and statistical techniques that provide a detailed
assessment of decision strategies and therefore allow for a more
convincing test of the superiority of – presumed– unconscious
processes.
Neuroscientific evidence for contextual effects
in decision making
doi:10.1017/S0140525X13000770
Kaisa Hytönen
Brain Research Unit, O.V. Lounasmaa Laboratory, Aalto University School of
Science, FI-00076 Aalto, Finland.
kaisa.hytonen@aalto.fi
Abstract: Both internal and external states can cause inconsistencies in
decision behavior. I present examples from behavioral decision-making
literature and review neuroscientific knowledge on two contextual
influences: framing effects and social conformity. The brain mechanisms
underlying these behavioral adjustments comply with the dual-process
account and simple learning mechanisms, and are weak indicators for
unintentionality in decision-making processes.
Newell & Shanks (N&S) criticize prior work on unconscious influences in decision making for providing insufficient assessment of
awareness, leading the authors to question whether unconscious
influences should be incorporated as prominent factors in decision
theories. While I appreciate their methodological concerns, I am
cautious to refute a large body of literature on automatic processes
in decision making (Chaiken & Trope 1999; Kahneman 2011;
Sloman 1996). I will explore the possible role of unconscious processing in decision making by discussing contextual influences in
judgment and choice.
There is a discrepancy between rational decision making, as
described by economic theory, and actual choices (Thaler 1980).
Both internal and external states (such as visceral factors,
framing, and social context) can induce inconsistencies in choice
behavior (Cialdini & Goldstein 2004; Loewenstein 1996;
Tversky & Kahneman 1981). Danziger et al. (2011a) report that,
prior to food breaks, judges in parole boards less frequently give
favorable decisions than after food breaks. The framing effect is
manifested, for instance, in the behavior of a majority of consumers who prefer a “75% lean” ground beef product over one
having 25% fat, even though there is no difference in the actual
product (Levin et al. 1985). Finally, related to the primes-to-behavior literature reviewed in the target article, descriptive social
norms are so powerful in directing behavior that people are
even willing to increase their own energy consumption to match
the consumption level of their neighbors (Schultz et al. 2007).
These examples raise many questions about the awareness and
intentionality of the decision maker. Why is consumer preference
affected by the positive or negative presentation of a piece of
information? Why would one use more energy than needed –
and pay for it – just because others use a lot of energy? And can
judges sleep at night peacefully knowing that someone else is
behind bars because they were hungry when they made their
parole decision? Economically, these choice biases do not make
sense, and based on the discussion following the publication by
Danziger et al. (2011a), the legal community objects to the idea
that meal breaks influence judicial decisions (Danziger et al.
2011b; Weinshall-Margel & Shapard 2011).
Recent neuroscience literature has shed light on the underlying
mechanisms of framing effects in situations where subjects choose
between a positively or negatively framed risky lottery versus a sure
outcome. This research suggests that framing effects are mediated
by emotional brain areas (amygdala), whereas resisting these effects
co-occurs with activation in the anterior cingulate cortex (ACC),
a brain region related to conflict detection (De Martino et al.
2006). These findings are consistent with the expectations of
dual-process theories, as they suggest an interplay between initial
emotional reactions (System 1) and suppressing control processes
(System 2) in the formation and resistance of framing effects,
respectively (Kahneman & Frederick 2007). Two further experiments have strengthened these claims. First, individuals with a
certain gene variant have a stronger coupling between the ACC
and amygdala and are able to resist framing effects better than
other individuals (Roiser et al. 2009). Second, people with autism
spectrum disorder do not show the same pattern of emotional
(skin conductance) responses to positive and negative frames compared with control subjects; they also exhibit weaker susceptibility
to framing effects (De Martino et al. 2008). Taken together, this
research indicates that largely inborn characteristics can influence
the strength of framing effects.
The tendency to follow the behavior of others has been proposed to be driven by error detection and subsequent adjustment
(Montague & Lohrenz 2007). Klucharev et al. (2009) tested this
hypothesis with functional magnetic resonance imaging in the
context of facial attractiveness estimation and found that
33
deviations from a stated general group opinion was associated with
activation in brain regions that also activate during erroneous
responses in simple trial-and-error tasks. The strength of the
“error response” was indicative of the subsequent behavioral
adjustment toward the group opinion. Intentional adaptation of
the reported attractiveness ratings is highly unlikely for two
reasons. First, due to extensive requirements for memory of
faces and attractiveness ratings, and second, because the behavioral adaptation is also reflected in the neural representation of the
stimulus value, suggesting a true modification of opinion beyond
mere social gratification (Zaki et al. 2011). Social context also
modifies the activation of the reward network for targets other
than faces and adjusts the neuronal representation of long-term
memories (Campbell-Meiklejohn et al. 2010; Edelson et al.
2011). Together, these findings suggest that social influence in
decision making is mediated by adapted value estimates and
memories.
The literature reviewed here unfortunately cannot give conclusive information about the presence or absence of unconscious
influence in the framing effect and social conformity, because
the experimental procedures did not include rigorous assessments
of awareness. Regardless, the gained knowledge gives many weak
indicators for unintentionality, if not unawareness. First, the neuroscientific findings of framing effects comply with the expectations of dual-process theories and show that inborn features
may influence the strength of behavioral framing effects, indicating that the decision process is systematically different between
groups of people in a simple and reasonably neutral decision
task. Second, even a single exposure to a descriptive social norm
can modify the value of an item possibly through basic and automatic reinforcement learning mechanisms. One noteworthy
aspect is that the dual-process accounts do not necessitate that
System 1 influence is uncontrollable in a top-down fashion
(Chaiken & Trope 1999). While in some conditions there might
be an unconscious effect, in other situations the influence of
framing or social norms can be intentionally acknowledged and
controlled by the decision maker.
A logical next step is to conduct further tests that measure a
decision maker’s awareness of the effect of framing and social
norms. N&S give many good pointers for designing methodologically sound experiments, but one should be careful not to influence the decision-making process with the awareness
assessment. Highlighting aspects of the decision-making task
can change the course of the decision-making process by increasing attention and top-down control.
ACKNOWLEDGMENT
Supported by the aivoAALTO project of the Aalto University.
Restrictive and dynamic conceptions of the
unconscious: Perspectives from moral and
developmental psychology
doi:10.1017/S0140525X13000782
Gordon P. D. Ingrama and Karolina Prochownikb
a
Bath Spa University, Newton Park, Newton St. Loe, Bath BA2 9BN,
United Kingdom; bJagiellonian University, Kraków 31-005, Poland.
g.ingram@bathspa.ac.uk
karolina.prochownik@uj.edu.pl
https://sites.google.com/a/bathspa.ac.uk/gordon-ingram/
Abstract: Newell & Shanks’ (N&S’s) conceptualization of the unconscious
is overly restrictive, compared to standard social psychological accounts.
The dichotomy between distal and proximal cues is a weak point in their
argument and does not circumvent the existence of unconscious
influences on decision making. Evidence from moral and developmental
psychology indicates that decision making results from a dynamic
mixture of conscious and unconscious processes.
34
Newell & Shanks (N&S) perform a valuable service in critically
reviewing some of the burgeoning literature on unconscious
decision making, and exposing some of the methodological weaknesses of these studies. Nevertheless, there are three related
aspects of their thesis that we would like to question: their conceptualization of the unconscious, their dichotomy between proximal
and distal cues, and their lack of consideration of any developmental evidence.
First, N&S’s treatment of “the unconscious” is conceptually very
limited, in that they appear to restrict unconscious decision making
to processes that are “cognitively impenetrable” (sect. 6.1.1) – that
is, that are not accessible to conscious awareness – without foregrounding this limitation on their definition. This is far from the
everyday and classical (e.g., Freudian) meanings of the unconscious as something that is potentially accessible to awareness,
but which usually occurs automatically and beneath the level of
conscious processing (Bargh & Morsella 2008). Indeed, the goal
of psychoanalysis is to make hidden or suppressed (temporarily
inaccessible) thoughts available once again to conscious awareness.
There are many examples from recent studies of moral reasoning and moral intuition that fit better with this classical understanding of the unconscious. For Haidt (2007, p. 998), for
instance, “Moral intuition refers to fast, automatic, and (usually)
affect-laden processes in which an evaluative feeling of goodbad or like-dislike (about the actions or character of a person)
appears in consciousness without any awareness of having gone
through steps of search, weighing evidence, or inferring a conclusion.” Note that this does not imply that someone cannot, on
reflection, come to an awareness of some of the steps that have
led him or her to this evaluative feeling. This paints a much
more dynamic picture of the unconscious than that presented
by N&S. And it is a picture that is shared with a researcher
whom the authors criticize, John Bargh, who has explicitly
stated that he does not view the unconscious in terms of a lack
of awareness of certain (subliminal) stimuli, but rather in terms
of a lack of awareness of the effects that certain stimuli have on
decision making (Bargh & Morsella 2008). So we may have a familiar case of authors from two different, but related, disciplines
(cognitive and social psychology) arguing across one another
because they are using the same word (“unconscious”) to refer
to two different but related phenomena (what is subliminal and
inaccessible to consciousness vs. what is merely overlooked or forgotten by consciousness).
This suspicion is reinforced by N&S’s reliance on a dichotomy
between “proximal” and “distal” cues (when assessing the relevance of a piece of information for decision making; sect. 1.2).
They claim an agent might be unaware of a distal cue (e.g.,
Mother always told me spinach was good for me) and its influence
on a current decision (to eat spinach), but still may be able to
justify her decision in terms of a proximal cue (the belief
spinach is healthy). It is hard to see the difference between a neglected distal cue and an unconscious influence on behavior, in the
naturalistic social psychological sense of Bargh and Morsella
(2008). Nor do N&S elaborate on what makes a distal cue “irrelevant” to decision making. Even long-past and long-forgotten cues
can be highly relevant: for example, a jury selection panel is unlikely to look favorably on any history of interaction between a
potential juror and a defendant, whether the juror can recall the
interaction or not. Social psychologists of like mind to Haidt and
Bargh would claim the authors confuse an agent’s ability to give
reasons for her own decision with her awareness of all the
factors that significantly influenced her decision. That can be
thrown into sharp relief by consideration of Haidt’s famous
cases of “moral dumbfounding” (e.g., when participants are
asked why they condemn a case of incest that has no harmful consequences): In such cases, individuals are unable to give either a
distal or a proximal cue for their behavior (Haidt 2001; cf.
Pizarro & Bloom 2003).
N&S’s other example of the relation between proximal and
distal cues (in an adjacent endnote) is even less satisfactory. We
are asked to consider a judge who sentences people with Afrocentric features more severely (without being aware of this bias)
because he perceives them as less intelligent. Yet this time, any
conscious justification given by judges cannot be the same as
the “proximal” cue, since the judges in these cases would presumably not rationalize their decisions in terms of any aspect of the
suspects’ appearance whatsoever: They would simply not be
aware that the perception of a face as either Afrocentric or unintelligent would influence their judgment. In such cases there is
clearly a disconnect between the conscious justification made by
an agent and causes of their behavior. In this context, it is interesting that a recent mock-jury study found that when given a chance
to deliberate, jurors were more likely to find an attractive defendant guilty, whereas without deliberation they were more likely
to find a non-attractive defendant guilty (Patry 2008). This
finding is in accord with a more dynamic picture of the unconscious than the one that N&S paint.
Finally, N&S omit any discussion of the extensive developmental literature on the ontogeny of children’s decision making. Yet
that literature is highly relevant to the conceptual limitations of
their article, in that there are many experimental tasks (reviewed
by Karmiloff-Smith 1992) for which younger children are unable
to articulate why they have a preference for a particular behavioral
choice, but for which older children are able to explain the conscious reasoning behind their choices. Because of this type of transition, developmental research currently represents a growing
point in dual-process accounts of decision making (e.g., Stanovich
et al. 2011). Taking a developmental perspective leads us again to
emphasize the dynamic relationship between conscious and
unconscious influences on decision making, because observing
how children solve certain tasks makes it clear that conscious
thought processes can come to modulate decision making that
was previously performed automatically.
Why decision making may not require
awareness
doi:10.1017/S0140525X13000794
I. P. L. McLaren, B. D. Dunn, N. S. Lawrence, F. N. Milton,
F. Verbruggen, T. Stevens, A. McAndrew, and F. Yeates
Washington Singer Laboratories, School of Psychology, University of Exeter,
Exeter EX4 4QG, United Kingdom.
i.p.l.mclaren@exeter.ac.uk
B.D.Dunn@exeter.ac.uk
Natalia.Lawrence@exeter.ac.uk
f.n.milton@ex.ac.uk
F.L.J.Verbruggen@exeter.ac.uk t.stevens@ex.ac.uk
am375@exeter.ac.uk fy212@exeter.ac.uk
Abstract: Newell & Shanks (N&S) argue against the idea that any
significant role for unconscious influences on decision making has been
established by research to date. Inasmuch as this conclusion applies to
the idea of an “intelligent cognitive unconscious,” we would agree. Our
concern is that the article could lead the unwary to conclude that there
are no unconscious influences on decision making – and never could be.
We give reasons why this may not be the case.
We begin by raising some general methodological issues regarding
the assessment of insight, and then we move to considering other
examples from our own work that also bear on the thesis of the
article. The first methodological issue we wish to raise regards
the possible knock on effects of measuring insight in the stringent
way that Newell & Shanks (N&S) recommend. Although we agree
that the immediacy criterion is well-motivated in principle, the
concurrent measurement of awareness with performance could
predispose participants to use a conscious decision strategy in a
situation where they may otherwise use an unconscious strategy.
Indeed, this criterion seems fundamentally at odds with N&S’s
recommendation that highly reflective situations should be
avoided in the study of unconscious decision making. The
authors argue that online judgments do not alter judgment strategies by citing a study which showed that the inclusion of an
online awareness measure made no difference to performance
(Lagnado et al. 2006), but in reaching this conclusion N&S are
relying on a null result (an approach they criticise when it provides
evidence in support of unconscious processes). Furthermore, the
absence of performance differences does not rule out the possibility that different processing strategies are being used to obtain
a similar level of performance in the two versions of the task.
The second methodological issue pertains to the narrative
rather than the systematic review approach that appears to have
been adopted in the article. We agree with N&S that a focus on
particular influential domains in such a review is entirely appropriate, but we feel that a systematic search strategy for identifying
studies in each domain should have been articulated. For
example, work we have conducted has found some evidence for
unconscious influences on a variant of the Iowa Gambling Task
(IGT) (Dunn et al. 2011) that we feel offers some support for
the unconscious account and would have been relevant here. In
particular, using the stringent insight criterion outlined by Maia
and McClelland (2004) that meet the reliability, relevance, immediacy, and sensitivity criteria, we found that participants behaviourally acquired a modified IGT task prior to being able to
articulate conscious awareness. Although the methodology of
this study can also be criticised (on the basis of low power), this
nevertheless is some evidence for unconscious decision-making
influences. Therefore, the review can be critiqued on the
grounds that its coverage of each domain is in parts selective.
The third methodological issue is that the insight literature has
generally neglected a potentially prominent role for individual
differences – namely, that individuals’ performance may be
more or less driven by unconscious influences. Consistent with
this position, verbal reports indicate that the degree to which performance on the IGT is driven by conscious awareness varies
between individuals (e.g., Guillaume et al. 2009, although we
acknowledge the limitations of the way awareness was indexed
in this study). Similarly, individual differences exist in the extent
to which anticipatory bodily signals (arguably a measure of unconscious influence) are associated with task acquisition on the IGT
and its variants (Dunn et al. 2011; Guillaume et al. 2009).
If marked individual differences do exist, this means that
attempts to characterise behaviour at the population level are
likely to be doomed to failure. In other words, the question
should shift from “is behaviour driven by unconscious influences?”
to “in which individuals and contexts is behaviour most driven by
unconscious influences?”
Turning now to examples taken from our own research that are
also relevant to this debate, our position is that there are other
types of unconscious influence on decision making, in particular
the influence of automatic, associative processes on behaviour.
The case is slightly complicated by the fact that associative processes do not have to proceed in the absence of awareness, but
equally they do not require it either (McLaren et al. 1994),
which immediately raises the possibility that there can be
instances of unconscious influences on decision making involving
processing of this type. We will focus on the demonstrations of
peak-shift in humans by Jones and McLaren (1999) and Livesey
and McLaren (2009), though we could equally appeal to demonstrations of implicit sequence learning by Jones and McLaren
(2009) and Spiegel and McLaren (2006), which make the point
that the decisions made by participants are quite different when
they are aware (a monotonic function consistent with rule use)
or unaware (a non-monotonic function consistent with peakshift) of the contingencies in play. Participants had to classify
green squares by pressing one of two keys. The participants
were not informed that the squares varied in either brightness
(1999) or hue (2009), and so the correct response had to be
learned by trial and error (they were given feedback). During a
subsequent test phase (without feedback) they were shown
35
stimuli that varied over a much wider range of brightness or hue.
At this point those participants that were unable to specify that the
correct attribute to guide their decisions exhibited the typical
“peak-shift” pattern of responding seen in similar experiments
with pigeons (e.g., Hanson 1957). Other participants who
became aware of the attributes relevant to responding (either
during training or testing) showed a different pattern, with performance improving monotonically as they moved from the training stimuli to more extreme values.
The awareness test used in this experiment clearly fails the sensitivity criterion that the authors would apply, and yet our point is
that there is actually a strong case to be made for this being an
example of unconscious influences on decision making. The key
here is the correlation between verbal report and the pattern of performance. If participants say they are aware of the critical attribute’s
role in the task, then they show one pattern. If they are not aware of
it, then they show a different pattern similar to that seen in pigeons.
N&S may still argue that both patterns of performance are due to
conscious cognitive processes; however, this would lead to an
entirely new interpretation of peak-shift in pigeons. If the explanation in terms of conscious cognitive processes is taken to apply
only to humans, then we must ask why such an unparsimonious position is being adopted, with one explanation for humans and
another for infra-humans. Either way, this type of evidence poses
a considerable challenge for the analysis offered in this article.
Better tests of consciousness are needed, but
skepticism about unconscious processes is
unwarranted
doi:10.1017/S0140525X13000800
Ryan Ogilvie and Peter Carruthers
Department of Philosophy, University of Maryland, College Park, MD 20742.
rogilvie@umd.edu
pcarruth@umd.edu
http://www.philosophy.umd.edu/Faculty/pcarruthers/
Abstract: What people report is, at times, the best evidence we have for
what they experience. Newell & Shanks (N&S) do a service for debates
regarding the role of unconscious influences on decision making by
offering some sound methodological recommendations. We doubt,
however, that those recommendations go far enough. For even if people
have knowledge of the factors that influence their decisions, it does not
follow that such knowledge is conscious, and plays a causal role, at the
time the decision is made. Moreover, N&S fail to demonstrate that
unconscious thought plays no role at all in decision making. Indeed,
such a claim is quite implausible. In making these points we comment
on their discussion of the literature on expertise acquisition and the
Iowa Gambling Task.
Newell & Shanks (N&S) argue that there is little reason to think
that unconscious thought plays a significant role in decision
making. But they cast their net of evidence too narrowly. In particular, we worry that the research they marshal in support of their
claim is either (a) consistent with competing interpretations that
are supported by other lines of research or (b) fails to show that
all elements that go into reaching a decision are conscious.
While the data that N&S cite show that we have more knowledge
of the factors that underwrite our decisions than was previously
thought, they do not show that this knowledge informs our
decisions or is conscious at the time the decision is made.
To begin, many of the studies that N&S cite demonstrate that,
at best, people have post facto knowledge of the factors that figure
in their decision making, not that those factors are conscious
during the decision-making process itself. Consider, for
example, N&S’s discussion of studies of expert decision making
that purport to find a discrepancy between explicit and implicit
policies, or between explicit and implicit knowledge of the
36
environmental cues that influence one’s decisions. Previous
studies had asked people to estimate their reliance on each cue,
finding little correlation between those judgments and actual
decision-making practice, thus suggesting that their reliance on
those cues was unconscious. But if people are presented,
instead, with a variety of sets of cue weightings and are asked to
select which most closely resembles the strategy they use, then
they prove quite accurate. N&S take this to show that people
follow a conscious decision strategy. However, that one has a
capacity to recognize one’s policy does not begin to show that
one consciously employs that policy. For the display may trigger
an implicit memory of the previously deployed, but an unconscious decision strategy, which then primes the person’s selection.
One reason to prefer this explanation is that it is a familiar finding
in the perception literature that masked stimuli produce implicit
memories, which can then modify behavior in future tasks (Leuthold & Kopp 1998; Schacter 1992).
Of course, we grant that this hardly settles the matter and that
further experimentation is required. In agreement with N&S’s
“immediacy” criterion, we suggest that what is needed in this
type of case is introspection sampling, where people are cued at
irregular intervals to report the contents of their conscious awareness (Hurlburt & Akhter 2006). Post facto measurements of conscious awareness not only run the risk of participants tapping into
implicit memories or accessing knowledge that played no active
part in the decision-making process, but they also run the risk
of participants forgetting what they had in fact been conscious of.
Similar points hold in connection with N&S’s discussion of the
Iowa Gambling Task. They point out that when Maia and McClelland (2004) use a more explicit and less open-ended set of probe
questions than had previously been employed, people show
awareness of which decks are the good ones, and of the approximate long-term payoffs of the decks, as soon as they start to
choose appropriately. N&S take this to show that it is conscious
knowledge of payoffs that drives people’s choices. Yet it may be
(as Maia & McClelland themselves note) that in their online selections people rely on their affective responses toward the various
options, without conscious judgments of relative goodness or
approximate payoffs playing any causal role in the process.
Indeed, when asked, people may base their judgments of goodness on their concurrent affective reaction while they contemplate
making a selection from each deck. Moreover, they may either be
able to reconstruct a rough estimate of the payoffs of each deck
from memory, or else they may have constructed such a model
in an ongoing manner following each trial.
Not only is this alternative construal of the data possible, but it
is preferable. For it can smoothly accommodate the findings from
patients with damage to ventromedial prefrontal cortex (VMPFC)
(which is widely accepted to be the primary site of cortical representations of affect; Rolls 1999). Such patients continue to
make bad choices in the Iowa Gambling Task, despite having
good knowledge of the expected payoffs from each deck
(Bechara et al. 2000). This finding makes good sense if people
base their selections on their affective responses (which are
absent or attenuated in VMPFC patients) while at the same
time building a conceptual model of the task contingencies. To
accommodate these data N&S will need to claim that in the
normal case it is conscious judgments of comparative goodness
that drive one’s affective reactions. We know, however, that affective responses occur quite swiftly. It is implausible to claim that in
every case they are preceded by conscious conceptual appraisals
of the situation.
Even if we set aside these concerns about N&S’s treatment of
the data, however, they will need to posit some mechanism that
can maintain an approximate running total of the net winnings
from each deck. Otherwise the judgments of comparative goodness and likely payoffs from each deck that they appeal to will
appear magical. But it is quite implausible that participants are
aware of calculating these approximate running totals during the
gambling task, in the way that one might be aware of calculating
the tip on a restaurant check. Much more likely is that participants
rely on an implicit system that operates unconsciously, perhaps
along the lines of Dehaene’s (1997) approximate number system.
In conclusion, N&S demonstrate, at best, that in normal
instances of decision making some components of the decisionmaking process are conscious. This is not surprising, given that
attentional resources are likely directed toward the task at hand,
and given the connections between attention, working memory,
and consciousness (Engle 2002; Knudsen 2007; Prinz 2012).
But it is now well established that working memory is a very
limited channel (Cowan 2000). It therefore beggars belief that
all of the factors that influence decision making could be conscious, if decision making occurs in real time.
Demonstrations of subconscious processing
with the binary exclusion task
doi:10.1017/S0140525X13000812
Navindra Persauda and Peter McLeodb
a
Faculty of Medicine, University of Toronto, Toronto, ON M5B 1X2, Canada;
Department of Experimental Psychology, University of Oxford, Queen’s
College, Oxford OX1 4AW, United Kingdom.
nav.persaud@utoronto.ca
peter.mcleod@queens.ox.ac.uk
b
Abstract: The binary exclusion task involves “subtle priming effects” and a
measure of awareness that is reliable, relevant, immediate, and sensitive.
This task, which meets the criteria outlined in the target article, has
been used to demonstrate subconscious processing.
The target article recommends that future research on subconscious processing “should focus on tasks in which participants’
attention is diverted away from the experimenter’s hypothesis,
rather than the highly reflective tasks that are currently often
employed” (Abstract). The authors also suggest that: “subtle
priming effects offer considerable promise for future exploration
of insight, awareness, and decision making” (sect. 6.3) and outline
four criteria for a measure of awareness: reliability, relevance,
immediacy, and sensitivity. The binary exclusion task meets
both of these recommendations and fulfills the four criteria for
a measure of awareness, but it is not mentioned in the target
article.
In each trial of the binary exclusion task one of two known
stimuli appears. Participants are required to respond with
the stimulus that was not shown (Debner & Jacoby 1994). If the
stimulus is presented for long enough, participants are aware of
which one was shown and respond correctly with the other one.
But at brief stimulus durations, participants are more likely than
chance to respond incorrectly, with the stimulus that was shown
(Persaud & McLeod 2007). The briefly presented stimulus must
have been processed because it affected the decision about
which response to make. But if it had been available to conscious
processes, the participant would have responded correctly with
the other stimulus. Thus the binary exclusion task with briefly presented stimuli demonstrates unconscious processing influencing
decision making.
The assessment of awareness in the binary exclusion task meets
all four of the criteria outlined in the target article. Since the
response used to assess awareness is the same as the behavioral
response, the assessment of awareness is reliable, relevant, and
immediate. Using the definition of sensitivity in the target
article (“same cues are provided for measuring awareness as for
eliciting behavior” in Table 1), the binary exclusion task is also sensitive because only one cue is presented.
Persaud and Cowey (2008) used a binary exclusion task to
compare the processing in the blind and sighted fields of the
blindsight patient, GY. A square wave grating was presented to
either the upper or lower quadrant of his blind or sighted field.
He had to reply with an exclusion response, saying “upper”
when it was in the lower field and “lower” when it was in the
upper field. He claimed to see the stimuli presented to his
sighted field and followed the exclusion instructions correctly.
But when the stimulus was presented to his blind field, he
reported that he was guessing, and his responses were significantly
more often than chance incorrect. That is, he responded that the
stimulus was in the quadrant in which it had been presented, not
in the opposite one. When the stimuli were made clearer (the contrast between light and dark bars of the grating increased) the discrepancy between blind and sighted fields became more marked.
He continued (unsurprisingly) to respond correctly to stimuli presented to his sighted field. But the probability of making an error
in the blind field (replying with the quadrant in which the stimulus
had been presented) increased. This is a clear demonstration of a
difference between conscious and subconscious processing.
Increasing the stimulus contrast decreased the error rate for consciously processed stimuli, but increasing the contrast increased
the error rate for subconsciously processed stimuli.
Just as with the normal participants tested with brief stimuli by
Persaud and McLeod, the experiment with GY demonstrates a
clear difference between conscious and unconscious processing.
Consciously processed stimuli lead to what James (1890)
described as “the pursuance of future ends,” correctly following
the experimenter’s instructions to respond with the stimulus
that was not shown. But subconsciously processed information
can lead to responses that are contrary to a person’s goals (Chalmers 1996; Searle 1992), erroneously responding with the stimulus that was shown.
Self-insight research as (double) model
recovery
doi:10.1017/S0140525X13000824
Tim Rakow
Department of Psychology, University of Essex, Colchester CO4 3SQ, United
Kingdom.
timrakow@essex.ac.uk
http://www.essex.ac.uk/psychology/department/people/rakow.html
Abstract: Self-insight assessment compares outcomes from two modelrecovery exercises: a statistical exercise to infer a judge’s (implicit) policy
and an elicitation exercise whereby the judge describes his or her
(explicit) policy. When these policies are mismatched, limited selfinsight is not necessarily implied: Shortcomings in either exercise could
be implicated, whereby Newell & Shanks’ (N&S’s) relevance or
sensitivity criteria for assessing awareness may not be met. Appropriate
self-insight assessment requires that both exercises allow the original
processes to be captured.
Slovic and Lichtenstein’s (1971) extensive review of research on
policy capturing for multiple-cue judgment (see Newell &
Shanks [N&S], sect. 2.2) was one of the first papers that I read
on human judgment; so one of the first things I learned about
human judgment was that people exhibit poor insight into their
cue use, overestimating the number of cues used and misestimating the relative weights applied to different cues. N&S have usefully reminded us that this conclusion is dependent upon a
particular choice of methods, and that the picture changes if
alternative methods for investigating self-insight are employed
(e.g., Reilly & Doherty 1989; 1992). When considering methods
for assessing self-insight for multiple-cue judgment, it is worth
noting that such investigations are an exercise in model recovery.
In fact, two model recoveries are required: (1) statistical techniques (typically regression) are used in an attempt to recover
the judge’s policy for making the original case judgments (the
implicit policy); and (2) the judge is also asked to recover his or
her strategy for making the original judgments (the explicit
37
policy) – often by assigning a points value to indicate each cue’s
weight. Conceptualising self-insight research as “double model
recovery” emphasises some limitations in previous work that
bear on N&S’s relevance and sensitivity criteria for the adequate
assessment of awareness (target article, Table 1), and points to
avenues for developing better methods for assessing self-insight.
First, this double-model-recovery framework highlights a critical question: why – as seems standard – instantiate the implicit
policy (from statistical model recovery) as the “correct” model,
and therefore assume that any discrepancy between implicit and
explicit policies represents the judge’s failure to recover the
“true” model? Model-recovery exercises in cognitive science
usually consider multiple “families” of candidate model. Typically,
lens model research considers a single family of models: compensatory linear rules that integrate a fixed number of cues – though it
does consider different family members, which differ according to
the number of cues used. Alternative families of non-linear (configural) or non-compensatory judgment models are less frequently
considered in the multiple-cue judgment literature, even though
several alternatives can be modelled, such as judgments made
according to the similarity of each case to a prototype, judgments
made following a non-exhaustive lexicographic search through
cues, and judgments where cues are selected probabilistically
and therefore different cues are used for different cases. In contrast, some research on multi-attribute choice does consider
different families of models: for instance, comparing alternative
models reflecting whether a compensatory or lexicographic
decision rule is being applied (e.g., Bröder 2003). Additionally,
this research on recovering choice processes highlights that different models reflecting quite distinct processes often fit the data
similarly well. Therefore, even when a compensatory linear
model fits the data, the judge may nonetheless have followed a
quite different process in making his or her judgments. In such
cases, any elicitation procedure that presupposes the compensatory linear combination of a fixed number of cues fails N&S’s relevance criterion because the behaviours being probed are not
those that drove the judgment. This is liable to generate a poor
match between the implicit and explicit policies. Thus, by following a restricted approach to modelling the judge to dictate the
constraints of that judge’s self-description, we create an insensitive
assessment of awareness and may misattribute poor modelling as
poor self-insight.
Second, a double-model-recovery framework emphasises the
potential for mis-recovery of the original judgment process by
either recovery technique (statistical or human). As many others
have done, I have pitted human judges against statistical rules in
multiple-cue judgments and – as is typical – have found that “statistical judges” outperform their human competitors (Dawes et al.
1989). However, in one investigation (see Rakow et al. 2003), our
statistical judge showed the same apparent lack of self-insight as
its human counterparts. A seven-cue predictive model derived
using logistic regression generated predicted probabilities (that
an applicant would be offered a place at a given university) for a
series of cases, each defined by multiple cues. Human judges
also provided the same set of judgments. Using the same linear
regression analysis applied to the human participants, the implicit
policy for the statistical judge declared only five cues to be used
reliably (i.e., significant). Thus the statistical judge showed the
typical pattern of limited self-insight that human judges display,
apparently overestimating the number of cues that it used!
Thus, just as assessments of awareness may fail N&S’s sensitivity
criterion, so too, insufficiently sensitive model recovery via linear
regression could contribute to an inappropriate conclusion of
“limited self-insight” (for a technical discussion of this problem,
see Beckstead 2007).
Third, we can consider strategies for assisting human judges in
recovering (describing) their judgment policies, which may, also,
influence the candidate models for the statistical element of the
double recovery exercise. In a recent study, we asked mental
health practitioners to self-identify with descriptions of alternative
38
families of judgment models – (non-)compensatory and (non-)
exhaustive – which drew on analogies to common decision aids
such as “balance sheets” and “trouble-shooting guides.” Many of
our assessors selected those options that implied contingent information search or non-compensatory information integration in
their own (triage) judgments. Thus, if required to describe themselves in terms of a compensatory model always using a fixed
number of cues (as per most self-insight research – though arguably failing the relevance criterion), inevitably some participants
were forced to misrepresent their policy. It would therefore be
unsurprising if judges displayed “poor self-insight.” Much work
has been done on alternative strategies for eliciting the subjective
weights for compensatory linear judgment policies (e.g., Cook &
Stewart 1975). However, we need improved (i.e., relevant and
sensitive) elicitation methods that allow for a wider range of information search and integration processes to be identified when
judges are asked to describe their judgment policies. Fair assessment of a judge’s self-insight requires that both the statistical exercise of deriving the implicit judgment policy and the elicitation
exercise whereby the judge describes his or her own judgments
allow – as far as possible – recovery of the processes by which
the original judgments were made.
What we (don’t) know about what we know
doi:10.1017/S0140525X13000836
Shlomi Shera and Piotr Winkielmanb,c
a
Department of Psychology, Pomona College, Claremont, CA 91711;
Department of Psychology, University of California, San Diego, La Jolla, CA
92093-0109; cUniversity of Social Sciences and Humanities, 03-815 Warsaw,
Poland.
Shlomi.Sher@pomona.edu
pwinkielman@ucsd.edu
http://psy2.ucsd.edu/~pwinkiel/
b
Abstract: The hypothesis of unconscious influences on complex behavior
is observationally equivalent to the dissociability of cognition and
metacognition (reportability). The target article convincingly argues that
evidence for unconscious influence is limited by the quality of the
metacognitive measure used. However, it understates the empirical
evidence for unconscious influences and overlooks considerations of
cognitive architecture that make cognitive/metacognitive dissociations
likely.
In their target article, Newell & Shanks (N&S) identify methodological problems in the study of unconscious influences on
decision making. Because awareness is indexed by subjects’
reports about what they know and how they know it, such
studies seek, in effect, to demonstrate dissociations of cognition
and metacognition: One component of the design shows that
information of some kind is influencing behavior in some way; a
second component elicits subjects’ reports about the information
they possess and the manner in which they are using it. Evidence
for unconscious influence is obtained when the (relatively indirect) cognitive measures and the (relatively direct) metacognitive
measures paint inconsistent pictures of the underlying mental
process.
As the authors note, such studies are only as compelling as the
metacognitive measures they use – and measures lacking in
reliability, relevance, immediacy, and sensitivity are often
employed. Indeed, research on unconscious influence suffers
from its own distinctive array of perverse incentives. As in other
areas of psychology, the researcher is driven to obtain evidence
for a clear effect on the cognitive measure. This incentive
fosters practices that make Type I errors more likely (Simmons
et al. 2011). In the study of unconscious influences, the researcher
typically has a second incentive – to fail to find evidence of a significant effect on the metacognitive measure. This incentive
may foster practices that make Type II errors more likely in the
assessment of awareness. One effective way of failing to detect
awareness is the use of unreliable, irrelevant, insensitive, and/or
belated probes. (Ironically, such bias in the choice of probes
might itself be unconscious.)
Complicating matters, it is not always easy to formulate,
let alone satisfy, the central criterion of relevance – that “assessments should target only information relevant to the behavior”
(target article, Table 1). What is relevant to a behavior depends
on what causes it. As a result, subtly flawed or imprecise causal
theories of behavior can lead even the well-meaning and careful
researcher to misidentify relevance in designing a measure of
awareness.
Although these methodological problems are important, and
although they challenge some influential findings, we believe
that the target article understates the full empirical and theoretical
case for unconscious influences on complex behavior. To make
the empirical case adequately would require a counter-review
rather than a commentary. Recent reviews of unconscious cognition that are more comprehensive and, in our view, also more
balanced are provided by Kouider and Dehaene (2007) and by
Simons et al. (2007). The empirical study of unconscious cognition
has its share of murky bathwater, but we believe the outlines of a
baby are distinctly discernible within it.
Critically, the target article also understates the theoretical case
for unconscious influences in complex behavior. N&S suggest that
such influences make for “good stories,” and that they confirm
“strong ex ante beliefs” about mental causation that soften the
critical judgment of researchers and journal editors. The explanatory role of unconscious influences is otherwise dismissed, as
when the authors state that we do not “need to posit ‘magical’
unconscious processes producing answers from thin air” (sect.
6.2). Are unconscious processes mere explanatory magic?
As we noted above, the hypothesis of unconscious influence is
observationally equivalent to the claim that cognition and metacognition are imperfectly coupled and sometimes strongly dissociate (because “conscious awareness” is measured by
metacognitive report). In this regard, unconscious processes are
no more “magical” than any other functional dissociation in cognition. Such processes are predicted by any cognitive architecture
that represents metacognition as a limited subset or partial
aspect of the mind.
For example, consider Baars’s (2005) global workspace theory
(GWT). Contrary to the target article’s cursory account of it,
this model is motivated by basic computational problems in cognition. Behavioral and neurophysiological investigations suggest the
existence of multiple semi-independent “modules” specialized for
different facets of information processing. This division of cognitive labor solves some problems, but it also creates a problem.
Specifically, information from different modules needs to be integrated to represent arbitrary perceptual combinations, solve unfamiliar problems not handled by any one module, organize motor
programs around a coherent action plan, and build an internal
model of “the self.” To address this problem, GWT proposes
that only a small subset of relevant information is selected for
“broadcasting” across the network. Integration is thus obtained,
but it is incomplete and comes at the expense of reduced information bandwidth and processing speed. The theory makes
sense of the local patterns of neural activity (with relative inactivity in globally connected association areas) that are observed in
subliminal priming experiments and in the behavioral automatisms of sleepwalking, epilepsy, and the vegetative state (Baars
2005). This architecture implies that non-selected information
can bias behavior, but without flexible integration or accessibility
to report.
Similar predictions are made by other models that, in principle,
distinguish the process of metacognitive report from other processes. For example, well-known models of learning and
memory distinguish between procedural and declarative systems
(Squire 1992). While the systems are thought to interact in the
control of complex behavior, declarative knowledge of procedural
mechanisms is at best indirect. Other models highlight constraints
on the (coarse-grained) format of metacognitive representations.
These constraints may limit the kinds of information that are available to report (Winkielman & Schooler 2011). Notably, dissociations are even possible within metacognition, as when we
overtly report that certain states are ineffable – we experience
more than we can overtly describe or express.
In any event, the claim that some cognitive operations are inaccessible to metacognition is not magical, but conceptually coherent and consistent with current knowledge. It predicts
systematic mismatches between cognitive processes and subjects’
overt reports about those processes – even when probes of awareness are reliable, relevant, immediate, and sensitive. Indeed, we
would be interested to see the authors propose a principled
sketch of a cognitive architecture in which cognition and metacognition are inseparable. To us, such a panpsychic architecture
sounds like magic.
Extremely rigorous subliminal paradigms
demonstrate unconscious influences on
simple decisions
doi:10.1017/S0140525X13000848
Michael Snodgrass, Howard Shevrin, and James A. Abelson
Department of Psychiatry, University of Michigan Medical Center, Ann Arbor,
MI 48105.
jmsnodgr@med.umich.edu
shevrin@med.umich.edu
jabelson@med.umich.edu
Abstract: While showing unconscious influences on complex decisions is
indeed difficult, relevant awareness in relatively simpler subliminal
paradigms is more easily assessed. Utilizing objective detection (vs. more
typical identification or classification) tasks to assess awareness
overcomes longstanding residual methodological problems, and prior
work using such methods (e.g., Snodgrass & Shevrin 2006) clearly shows
unconscious influences on simple decisions.
Newell and Shanks (N&S) marshal impressive evidence that
claims for unconscious influences on decision making are likely
premature in the three areas they primarily discuss. Especially
in such complex paradigms (e.g., multiple-cue judgment), we
agree that it is very difficult to adequately assess relevant awareness, and hence that the jury is (or should be) still out. In contrast,
however, subliminal paradigms are simpler, making assessing
relevant awareness easier. Of course, such paradigms also face
methodological hurdles, and we agree that much subliminal
work does not overcome these difficulties. Still, contra N&S, we
argue that subliminal paradigms can demonstrate unconscious
influences on simple decisions under certain conditions. We first
summarize our methodological analysis (cf. Snodgrass et al.
2004a, pp. 849–53), and then briefly describe some prior work
that meets these extremely stringent methodological criteria.
We focus on objective threshold paradigms, wherein performance
on awareness assessment tasks does not exceed chance (i.e., d′=0).
Skeptical interpretations are more plausible in subjective
threshold paradigms, wherein performance exceeds chance and/
or awareness assessment is often weak (e.g., post-experimental
inquiries).
How should relevant awareness be assessed? All stimulusrelated effects (e.g., semantic priming), whether conscious or
unconscious, require at least partial stimulus identification.
Accordingly, forced-choice prime identification tasks (e.g., “Was
it word A or word B that was just presented?”) adequately
assess awareness in principle, because any conscious partial identification will raise performance above chance. For example, given
“happy” and “terror” as response alternatives, perceiving the letter
“t” would favor the latter response. Consequently, demonstrating
39
null identification awareness warrants claims for unconscious
effects on other (e.g., priming) tasks.
Perhaps surprisingly, however, widely used categorization tasks
(e.g., “Was the just-presented positive or negative?”) may not be
adequate because they frequently require more extensive identification. Here, for example, perceiving “t” alone would be
useless. Given this, partial identification could conceivably activate
related versus unrelated prime words, yet be insufficient to allow
conscious categorization. Lovibond and Shanks (2002, p. 9) made
related arguments, but failed to recognize this critical difference
between identification versus categorization, instead lumping
them together as “recognition” (cf. Wong et al. 2004, p. 239).
Unfortunately, however, even given observed chance identification, intrinsic measurement error makes definitively ruling
out relevant awareness difficult (cf. the null sensitivity
problem), because true identification might exceed chance.
While there are various potential solutions, here we suggest a
simple yet underutilized alternative: Use true detection tasks
(e.g., “Was a word or a blank field just presented?”) instead,
arranging conditions such that detection d′ ≈ 0. Signal detection
theory (Green & Swets 1966) provides strong reasons to believe
that such tasks exhaustively assess even partial identification (cf.
Macmillan 1986). Even more important, much evidence (Snodgrass et al. 2004a, p. 853) shows that objective detection
thresholds (ODTs) are well below objective identification
thresholds (OITs), and are hence extremely conservative. To
illustrate, Fisk and Haase (2011) obtained both detection
and identification for masked words under various exposure
durations. See Fig. 1.
As Fig. 1 shows, given Fisk and Haase’s masking conditions,
detection d′ ≈ 2 at the OIT (i.e., identification d′ ≈ 0 with
25 ms exposures) and remains well above chance even at belowOIT durations. Critically, then, if the ODT is approximated, we
can be very confident that extremely stringent OIT conditions
are also satisfied, even given measurement error in detection
d′ – thus solving the null sensitivity problem outright regarding
ruling out conscious partial identification.
But what about other possible artifacts cited by N&S, such as
Pratte and Rouder’s (2009) claim that standard prime discrimination tasks underestimate their visibility in the priming phase?
While we disagree with their fundamental reasoning, here we
simply note that their ostensibly improved awareness assessment
procedure only increased identification from 54% to 60%, a d′
increase of about 0.3. Thus, even if they are correct,
approximating the ODT would still guarantee that extremely stringent OIT conditions were simultaneously met.
ODT effects are large and reliable. Although most grant that
ODT conditions are extremely stringent, they are rarely utilized
in recent years, in part because they are difficult to achieve
using typical computer monitors. Perhaps more importantly,
many investigators believe that all effects will disappear given
the extremely brief exposures necessary for ODT conditions.
Refuting such intuitions, however, our meta-analysis (Snodgrass
et al. 2004b) shows that ODT effects are both large (Cohen’s
d = 0.88) and reliable (p ≈ 10−9).
But what about unconscious effects on decision making
proper? In recent years subliminal investigations of cognitive
control processes have increased markedly, obtaining positive
results with clearly decision-relevant paradigms such as go/no go
(cf van Gaal et al. 2012 review). However, because virtually all
of these studies used identification or classification tasks to
assess awareness, they do not conclusively rule out conscious
partial identification.
In a series of ODT studies, however, we (Snodgrass et al. 1993a;
1993b; Snodgrass & Shevrin 2006) investigated unconscious influences on forced-choice identification itself, which clearly requires
simple decision making. For the current purposes, the most striking result was that under certain conditions identification was
reliably below chance. This requires that the stimuli were unconsciously identified, yet rejected as a response (i.e., excluded – cf.
Jacoby 1991). Otherwise, only chance performance would
result. But could this effect be conscious? No, because (1) ODT
conditions rule out conscious partial identification; (2) conscious
exclusion requires much stronger stimuli (i.e., exceeding the subjective threshold; Debner & Jacoby 1994); and (3) instructions
requested correct responses (i.e., inclusion, not exclusion).
Further, these effects have been repeatedly replicated, both by
skeptical investigators (Van Selst & Merikle 1993) and including
critical tests for possible artifacts. Accordingly, these effects
reflect unconscious, involuntary exclusion. While still relatively
simple, this result is notable indeed, given that response inhibition
(i.e., exclusion) is a core decision process long thought to require
relevant awareness.
It may be that only relatively simple decisions can be unconsciously influenced. Further subliminal work using more
complex paradigms (cf. van Gaal et al. 2012) under extremely rigorous ODT conditions, however, are essential before firm conclusions about unconscious influences on complex decision
making can be drawn.
Even “unconscious thought” is influenced by
attentional mechanisms
doi:10.1017/S0140525X1300085X
Narayanan Srinivasana and Sumitava Mukherjeeb
a
Centre of Behavioral and Cognitive Sciences, University of Allahabad,
Allahabad 211002, India; bIndian Institute of Technology Gandhinagar,
Ahmedabad, 384424, India.
nsrini@cbcs.ac.in
http://facweb.cbcs.ac.in/nsrini/
sumitava.inbox@gmail.com
https://sites.google.com/site/sumitavaehome/
Figure 1 (Snodgrass et al.). Sensitivity (d′) of detection and
identification as a function of masked word (target) exposure
duration. Reprinted by permission from Fisk and Haase (2011).
40
Abstract: In this commentary, we focus on the role of attentional
mechanisms in unconscious thought. We argue that even distracted or
unconscious thought is capacity limited and differences in scope of
attention influence processing during unconscious thought. Attention
also would influence processes at different stages in the proposed lens
model. We conclude that there is a clear need to understand the role of
attention to better understand conscious or unconscious thought.
Recently multiple attempts have been made to argue in favor of
powerful unconscious processes affecting decision making,
including recommendations to let the unconscious guide one’s
decisions (Dijksterhuis & Nordgren 2006). Newell and Shanks
(N&S) have done a commendable job questioning the efficacy
of seductive claims regarding unconscious processing. We focus
on the Unconscious Thought Theory (UTT) and paradigms used
to argue for deliberation-without-attention. Although we agree
with the broad claims made in the target article against UTT,
N&S do not directly address the role of attentional mechanisms
involved in distracted or supposedly unconscious thought. The
role of attentional processes in the lens model discussed by
N&S is also not clear, although they point to attentional or topdown mechanisms influencing processes such as motion perception. We think the role of attention is extremely critical for the
debate on conscious and unconscious thought, and that it requires
further elaboration. We suggest rethinking the core assumptions
of UTT, the very definition of unconscious thought and the
nature of processing during distraction.
A critical assumption of UTT is that the powerful unconscious is
not constrained by limited capacity attentional processes (Dijksterhuis & Nordgren 2006). We have questioned the unlimited
capacity and optimal weighing assumptions of UTT using simulations that were performed on data sets employed in the UTT
paradigms (Srinivasan & Mukherjee 2010). The simulations
clearly showed that a small subset of information is sufficient to
produce performance that is seen in UTT tasks. Experimental
results (Ashby et al. 2011) confirm our concern with the fundamental assumptions of UTT (the capacity and weighting principles). The use of a generic “sub-sampling” heuristic can enable
people to “select” a small set of dominant attributes during distracted thought (hypothesized to elicit “unconscious thought”)
and can partly explain other related findings based on consideration sets even during conscious thought (Mukherjee & Srinivasan
2013). This is consistent with suggestions made by N&S to explain
earlier findings on decision making (Wilson & Nisbett 1978). Even
intuitive or affective processing (Usher et al. 2011; Kahneman
2011) could be utilizing a subset of attribute information along
with quickly recovered cues from memory that would result in
decently good solutions because many choice scenarios require
attending only to a subset of the information. For example, in
Usher et al.’s (2011) data set consisting of 12 attributes and four
alternatives, choice based on the two best attributes creates a tie
between the best and second best option, and selecting 7 out of
the possible 12 attributes results in the selection of the best
alternative. These results indicate that attention plays an important role in selecting specific attributes based on prior experience
to make satisfying decisions.
A critical problem in many decision making studies is the lack of
proper treatment of attentional processes, possibly linked to
graded differences in consciousness. For example, attention is
used to operationalize “unconscious thought,” which we believe
conflates attention and consciousness and treats attention as a
dichotomous variable (Mukherjee & Srinivasan 2013; Srinivasan
& Mukherjee 2010), thereby limiting the construct of attention.
Attention can vary as a resource (more versus less) and perceptual
scope (focused versus distributed) that involves differences in
selection resulting in differences in perception, memory, and
awareness (Baijal & Srinivasan 2009; Srinivasan et al. 2013).
Given that people are performing a distractor task during
“unconscious thought,” the nature of the distractor task – and
more specifically the attentional mechanisms employed during
distraction – can potentially influence processing either during
or after distraction. The changes in (perceptual or conceptual)
scope of attention under different situations enable us to sample
and process information differently leading to differences in processes involved in memory and decision making. Using the UTT
paradigm, we manipulated the distraction task using global or
local processing (associated with changes in scope) at low and
high levels of cognitive or working memory load (Srinivasan
et al. 2013). We found that global processing during distraction
resulted in stronger preference for the chosen item irrespective
of cognitive load. In addition, we found better incidental
memory for attributes with global compared to local processing
during distraction only when the distractor was an easy low load
task.
Therefore we propose that the putative “unconscious thought”
is constrained by differences in the attentional processes
employed during distraction (Mukherjee & Srinivasan 2013; Srinivasan et al. 2013) and to differences in selection (e.g., information
sampling; Srinivasan & Mukherjee 2010). Theorizing about the
causal effects of conscious versus unconscious processes is critically dependent on a proper treatment of attention (like the
global workspace theory discussed in the target article). Conflating
both attention and consciousness would add more confusion to
this critical debate on the role of consciousness in decision
making.
More generally, attention could influence judgment and
decision making at multiple points such as cues and their utilization – points C and D in the lens model. Selective attention
mechanisms (e.g., subsampling) can affect the number of cues
selected for processing depending on the weights of the cues
and past experience of their validity. Sometimes not attending
to part of the information or relying on small samples can prove
useful (Gigerenzer & Gaissmaier 2011). The differences in selection (changes in scope of attention) could also affect utilization of
the cues and would be consistent with results showing that
changes in scope of attention can affect preference strengths
and memory (Srinivasan et al. 2013).
The information that we attend to gets privileged access in
working memory (McElree 2006) and the access can be related
to graded levels of consciousness as attention and working
memory interact (Baars & Franklin 2003). A clear understanding
about the role of different attentional processes is crucial for
debates on the role of consciousness in decision making including
the current analysis about causally effective unconscious processes. Much of the published literature in UTT and other areas
of decision making (see Mukherjee & Srinivasan 2013) need to
be reevaluated through the lens of attentional mechanisms and
their role in conscious or unconscious thought.
Performance and awareness in the Iowa
Gambling Task
doi:10.1017/S0140525X13000861
Helen Steingroever and Eric-Jan Wagenmakers
University of Amsterdam, 1018 XA Amsterdam, The Netherlands.
helen.steingroever@gmail.com
ej.wagenmakers@gmail.com
www.helensteingroever.com
www.ejwagenmakers.com
Abstract: Newell & Shanks (N&S) conclude that healthy participants
learn to differentiate between the good and bad decks of the Iowa
Gambling Task, and that healthy participants even have conscious
knowledge about the task’s payoff structure. Improved methods of
analysis and new behavioral findings suggest that this conclusion is
premature.
Newell & Shanks (N&S) convincingly argue that past research has
severely overstated the importance of conscious processes in
decision making. We agree with N&S on many counts, but here
we focus on what is perhaps our sole source of dissention. N&S
conclude that healthy participants who perform the Iowa Gambling Task (IGT) learn to differentiate between the good and
bad decks, and that this behavioral differentiation is even reflected
in conscious knowledge about the payoff structure. We believe
this conclusion may be premature: Several pitfalls in IGT data
analysis methods frustrate a fair interpretation of IGT data, and
several behavioral findings go against the authors’ conclusion.
41
Figure 1 (Steingroever & Wagenmakers). Choice behavior of healthy participants in Fridberg et al. (2010), once for the good and bad
decks (left panel) and once for each deck separately (right panel). Each block contains 20 trials, except the last block (15 trials).
The first pitfall is that the traditional way of analyzing IGT
data is incomplete and potentially misleading because it collapses
choice behavior over the two good decks and over the two bad
decks. This procedure hides the impact of the frequency of
losses (bad deck B and good deck D yield rare losses, whereas
bad deck A and good deck C yield frequent losses) and potentially obscures diagnostic information. For example, consider
the data of Fridberg et al.’s (2010) healthy participants. Fridberg
et al. plot the mean proportion of choices from the good and bad
decks as a function of trial number, replotted here in the left
panel of Figure 1. This panel suggests that participants learn
to prefer the good decks. However, Fridberg et al. also plot
the mean proportion of choices from each deck separately,
replotted here in the right panel of Figure 1. This panel shows
that, across all trials, participants prefer the decks with infrequent losses (B & D).
A similar problem is evident in work that assesses conscious
knowledge about the IGT either with subjective experience
ratings (C+D) – (A+B) (Bowman et al. 2005; Cella et al. 2007),
or by determining whether participants have conscious knowledge
that would encourage them to choose one of the two best decks
(Maia & McClelland 2004). However, participants who consider
“one of the best decks as the best deck” do not necessarily understand that there are two best decks and that both bad decks should
be avoided. To investigate whether participants understand that
there are two good decks, participants should identify the best
and second-best deck on each trial.
The final pitfall concerns the way in which IGT studies typically
assess the learning process, namely by applying an analysis of variance to assess whether participants’ preference for the good
decks (i.e., (C+D) – (A+B)) increases over blocks of trials (main
effect of block). A significant effect of block is typically taken as
evidence that participants learned to discriminate between the
good and bad decks. However, when the main effect of block is
significant, this does not imply it is also substantial. For
example, consider the data of Bowman et al. (2005), who tested
three groups of healthy participants that differed in whether
they obtained a manual or computerized IGT combined with or
without a 6-second delay. The only significant effect was a main
effect of block. However, even in the last block (i.e., the final 20
trials), the three groups showed at most a weak preference for
the good decks, as (C+D) – (A+B) ranged from about 3 to about
6.5. A value of 3 corresponds to an average of 11.5 out of 20
choices from the good decks, and a value of 6.5 corresponds to
an average of 13.25 out of 20 choices from the good decks.
Similar unconvincing results were evident from subjective
ratings of how positive each deck was experienced. These findings
42
suggest that neither participants’ behavioral preference for the
good decks nor their conscious preference for the good decks is
substantial. Cella et al. (2007) reported similar findings.
Next to the above mentioned pitfalls, several behavioral findings contradict the conclusion from N&S. First, a detailed reanalysis of eight data sets showed that healthy participants learn
to prefer the good decks in only one data set (see Steingroever
et al. 2013, and references therein). In the remaining seven data
sets, participants either only learn to avoid bad deck A (frequent
losses) or prefer the decks with infrequent losses (decks B & D).
Such a preference for the decks with infrequent losses – the
frequency-of-losses effect – has been reported by many studies.
The empirical evidence for the frequency-of-losses effect contradicts the assumption that healthy participants learn to prefer the
good decks.
Second, Steingroever et al. (2013) showed that participants
have a tendency to switch frequently throughout the entire task.
This is counterintuitive because one expects a strong decrease
in the mean number of switches once participants learned to
prefer the good decks. The frequent switches suggest that participants do not learn to systematically differentiate between the good
and bad decks, a suggestion that is illustrated by deck selection
profiles of 394 participants (Steingroever et al. 2013; see https://
dl.dropbox.com/u/12798592/DeckSelectionProfiles.zip for the
deck selection profiles); each participant has a highly idiosyncratic
choice pattern, and for most participants it is impossible to identify a point where they realized that the good decks should be
preferred.
In sum, detailed analyses of IGT data have shown that even
healthy participants are unable to discriminate the good decks
from the bad decks, a finding that suggests a lack of both conscious
and unconscious knowledge in this task.
The problem of the null in the verification of
unconscious cognition
doi:10.1017/S0140525X13000873
Eric Luis Uhlmann
HEC Paris – School of Management, Management and Human Resources
Department, 78351 Jouy-en-Josas, France.
eric.luis.uhlmann@gmail.com
Abstract: Newell & Shanks (N&S) argue that when awareness measures
are more reliable and valid, greater evidence of awareness of supposedly
unconscious influences is revealed. A related issue is that unconsciousness
is typically the null hypothesis that evidence of awareness will not emerge.
As it is difficult to conclude the null, it is also difficult to conclude a lack of
conscious awareness.
Traditional theories hold that human beings make decisions consciously and intentionally. In contemporary cognitive science, this
traditional perspective has been challenged by research pointing
to an important role for unconscious influences in decision
making. Newell & Shanks (N&S) provide a methodological critique of some of the major bodies of literature on unconscious
cognition. As they argue, when awareness probes are more
reliable, relevant, immediate, and sensitive, greater evidence of
conscious awareness is sometimes revealed. Thus, at least some
findings used to argue for unconscious influences on decision
making may result from shortcomings of commonly used
measures of conscious awareness.
This point also applies to some areas of research on implicit and
automatic cognition not addressed by N&S. Uhlmann et al. (2008)
raised similar concerns about methodological limitations potentially
shrouding evidence of awareness of the automatic associations
measured by tasks like the Implicit Association Test (IAT; Greenwald et al. 1998). Early studies finding negligible correlations
between self-reported attitudes and IAT scores led to the conclusion that the latter measured attitudes of which the person
was not consciously aware. However, subsequent studies reveal
robust implicit-explicit correlations after correcting for random
measurement error (Cunningham et al. 2001; Cunningham et al.
2004) and when more relevant self-report measures are used
(e.g., asking the person to report their automatic gut feelings
rather than their explicit attitudes; Ranganath et al. 2008; Smith
& Nosek 2011). Moreover, correlations between self-report and
implicit measures are higher in domains that are less socially sensitive (e.g., consumer as opposed to racial attitudes; Nosek 2005), for
participants unmotivated to conceal their true attitudes (Banse et al.
2001; Dunton & Fazio 1997; Fazio et al. 1995; Payne 2001; Payne
et al. 2005), and when respondents are encouraged to be honest in
their self-reports (Olson et al. 2007). This suggests that people are
at least partly aware of their automatic associations, but that insufficiently reliable, sensitive, and relevant measures often obscure this
fact (Uhlmann et al. 2008).
This reflects not just methodological limitations of the measures
involved, but also the broader problem with operationalizing
unconsciousness as a null effect (as is done in most research on
unconscious cognition). When post hoc debriefings do not
uncover evidence of awareness, or a correlation between a selfreport measure and implicit measure fails to emerge, such null
effects are used to conclude a lack of conscious awareness. This
would be less problematic if we knew a priori that the measure
of awareness were perfectly valid. However, how does one really
know whether an awareness measure is reliable, relevant, sensitive,
or immediate enough? Such measures are most self-evidently valid
when they uncover some evidence of conscious awareness. But
when they do not, should we then conclude the null (i.e., a lack
of conscious awareness), or worry that the measures involved are
not good enough? Equating unconsciousness with the null also
leaves the findings vulnerable to criticism. A skeptic can always
argue (in some cases post hoc) that the awareness measure was
insufficiently relevant, reliable, immediate, or sensitive.
Importantly, there is considerable evidence of unconscious influences on decision making that is not dependent on null effects. For
instance, research on the effects of “reasons analyses” shows that
asking people to provide reasons for their attitudes leads them to
report different attitudes, suggesting that they do not actually
know what the real reasons for their attitudes are (Wilson &
LaFleur 1995; Wilson et al. 1984; 1989). N&S argue that reasons
analyses may lead participants to incorporate additional information
they had not considered before and therefore change their attitudes, but they offer no evidence that this actually occurs. Moreover, it is unclear why consciously incorporating previously
unconsidered information would reduce attitude-behavior consistency and post-choice satisfaction if the influence of the new information (and resulting attitude change) is genuine. Thus, the effects
of reasons analyses are more consistent with a lack of full introspective access into the true influences on one’s attitudes.
In addition, the effects of unobtrusively presented primes (e.g.,
words related to competition) on relevant judgments and actions
(e.g., competitive behavior) have been replicated in scores of
studies (for reviews, see Bargh 2006; Bargh et al. 2012; DeCoster
& Claypool 2004; Wheeler & DeMarree 2009). Indicating that
such influences occur primarily when participants are unable to
consciously resist them, priming effects have been shown to
attenuate or even reverse when study participants suspect they
are being influenced (Lombardi et al. 1987; Newman & Uleman
1990) or their awareness of the primes is experimentally increased
(Erb et al. 2002; Newman & Uleman 1990; Strack et al. 1993). If
increased awareness reduces priming effects, then assimilation to
primes is almost certainly unconscious. The case that people are
unaware of the influences of primes on their judgments and behaviors does not rest solely on null effects.
As highlighted by N&S, the criteria currently used to demonstrate unconscious cognition are worth critiquing and debating.
However, it is worth discussing not only the criteria for concluding
an influence on decision making is unconscious, but also for concluding it is conscious. When strong evidence of unconscious cognition is unavailable, researchers should not assume conscious
awareness by default. Rather, awareness should be demonstrated
empirically. A liberal criterion is a statistically significant relationship between an awareness probe and the phenomenon of interest. A conservative criterion is that an effect only emerges among
participants who report being consciously aware of it. Regardless
of what criteria the field ultimately settles on, it is critical that the
burden of proof for concluding consciousness and unconsciousness be equally difficult to meet.
What makes a conscious process conscious?
doi:10.1017/S0140525X13000885
Max Velmans
Goldsmiths, University of London, New Cross, London SE14 6NW, United
Kingdom.
m.velmans@gold.ac.uk
http://www.gold.ac.uk/psychology/staff/velmans/
Abstract: Newell & Shanks’ (N&S’s) critical review considers only a very
limited sense in which mental processes can be thought of as either
conscious or unconscious and consequently gives a misleading analysis of
the role of consciousness in human information processing. This
commentary provides an expanded analysis of conscious processing that
also reveals the various ways in which mental processes are unconscious.
According to Newell & Shanks (N&S), it now seems to be generally accepted that whether a process is conscious should be operationally defined in terms of whether one has reportable
knowledge of it. Consequently, in their critical review of unconscious processing, this is the criterion that they apply. However,
as I have noted in the pages of this journal and elsewhere (see
Velmans 1991a; 1991b; 1993; 1996; 2009), the psychological and
philosophical literature often confounds three distinct senses in
which a process might be said to be conscious. It might be conscious:
a. in the sense that one is conscious of the process,
b. in the sense that the operation of the process is
accompanied by consciousness (of its results), and
c. in the sense that consciousness enters into or causally influences the process.
43
In these publications I have also considered how these different
forms of “conscious processing” apply to every stage of human
information processing, although I only have space in this brief
commentary to deal with a few examples. Ask yourself what’s conscious about “conscious verbal thought.” We do not have introspective access to how the preconscious cognitive processes that
enable thinking produce individual, conscious thoughts in the
form of “inner speech.” However, the content of such thoughts
and the sequence in which they appear does give some insight
into the way the cognitive processes (of which they are manifestations) operate over time in problem solving, thinking, planning
and so on. Consequently such cognitive processes are partly conscious in sense (a), but only in so far as their detailed operation is
made explicit in conscious thoughts, thereby becoming accessible
to introspection and consequent report. As noted above, being
conscious in sense (a) is the criterion that N&S adopt.
However, many psychological processes are conscious in sense
(b), but not in sense (a) – that is, we are not conscious of how the
processes operate, but we are conscious of their results. This
applies to perception in all sense modalities. When consciously
reading this sentence, for example, you become aware of the
printed text on the page, accompanied perhaps by inner speech
(phonemic imagery) and a feeling of understanding (or not), but
you have no introspective access to the processes which enable
you to read. Nor does one have introspective access to the details
of most other forms of cognitive functioning, for example, to the
detailed operations that enable “conscious” learning, remembering,
engaging in conversations with others, and so on.
Crucially, having an experience that gives some introspective
access to a given process, or having the results of that process manifest in an experience, says nothing about whether that experience
carries out or controls that process. That is, whether a process is
“conscious” in sense (a) or (b) needs to distinguished from
whether it is conscious in sense (c). Indeed, it is not easy to envisage how the experience that makes a process conscious in sense
(a) or (b) could make it conscious in sense (c). Consciousness of
a physical process does not make consciousness responsible for
the operation of that process (watching paint dry does not actually
make it dry on the wall). So, how could consciousness of a mental
process carry out the functions of that process? Alternatively, if
conscious experience results from a mental process, it arrives too
late to carry out the functions of that process. For example, try
reading the following sentence and note what you experience:
If we don’t increase the dustmen’s wages, they will refuse to take the
refuse.
Note that on its first occurrence in your phonemic imagery or
“inner speech,” the word “refuse” was (silently) pronounced with
the stress on the second syllable (refuse) while on its second occurrence the stress was on the first syllable (refuse). But how and when
did this allocation of stress patterns take place? Clearly, the syntactic and semantic analysis required to determine the appropriate
meanings of the word “refuse” must have taken place prior to the
allocation of the stress patterns; and this, in turn, must have
taken place prior to the phonemic images entering awareness.
Note too, that while reading, one is not conscious of any of the
visual processing or pattern recognition that is required to identify
individual words, or of any syntactic or semantic analysis being
applied to the sentence. Nor is one aware of the processing
responsible for the resulting inner speech (with the appropriate
stress patterns on the word “refuse”). The same may be said of
the paragraph you are now reading, or of the entire text of this
commentary. You are conscious of what is written, but not conscious of the complex input analyses involved. And if you are
not conscious of how these processes operate, in what sense can
they be said to be under “conscious control”?
The same may also be said about the detailed operation of
nearly every other form of human information processing (see
reviews cited above). According to N&S, “evidence for the existence of robust unconscious influences on decision making and
44
related behaviors is weak, and many of the key research findings
either demonstrate directly that behavior is under conscious
control or can be plausibly explained without recourse to unconscious influences” (sect. 6.4). On the contrary, evidence for the
influence of unconscious mental processes on human behavior
is ubiquitous. Indeed, if these complex processes were conscious
in the sense that N&S intend, they should be directly available to
first-person introspection, in which case there would be no need
to discover their operation by means of laborious, third-person,
cognitive psychological research!
The effect of the cognitive demands of the
distraction task on unconscious thought
doi:10.1017/S0140525X13000897
Laurent Waroquier,a Marlène Abadie,a Olivier Klein,b and
Axel Cleeremansc
a
CLLE, Université de Toulouse II, 31058 Toulouse, France; bCenter for Social
and Cultural Psychology, Université Libre de Bruxelles, 1050 Bruxelles,
Belgium; cCenter for Research in Cognition & Neurosciences, Consciousness,
Cognition & Computation Group, Université Libre de Bruxelles, 1050
Bruxelles, Belgium.
laurent.waroquier@univ-tlse2.fr
http://clle-ltc.univ-tlse2.fr/accueil-clle-ltc/annuaire/post-doctorants/
waroquier-laurent/
marlene.abadie@univ-tlse2.fr
http://clle-ltc.univ-tlse2.fr/accueil-clle-ltc/annuaire/doctorants/abadiemarlene/
oklein@ulb.ac.be
http://www.psycho-psysoc.site.ulb.ac.be/equipe/olivier-klein
axcleer@ulb.ac.be
http://srsc.ulb.ac.be/axcwww/axc.html
Abstract: The unconscious-thought effect occurs when distraction
improves complex decision making. Recent studies suggest that this
effect is more likely to occur with low- than high-demanding distraction
tasks. We discuss implications of these findings for Newell & Shanks’
(N&S’s) claim that evidence is lacking for the intervention of
unconscious processes in complex decision making.
Recent studies suggest that performing a distraction task concurrently with a complex decision paradoxically improves the quality
of the decision (e.g., Dijksterhuis et al. 2006b). This finding,
referred to as the unconscious-thought effect (e.g., Strick et al.
2011) or as the deliberation-without-attention effect (see
below), has been interpreted as evidence that complex decisions
are best made through “unconscious thought.”
In early theoretical accounts, Dijksterhuis and colleagues
defined unconscious thought as thought processes occurring
outside conscious awareness and proposed that unconscious
thought is capacity-unlimited (Dijksterhuis 2004; see also Dijksterhuis & Nordgren 2006). Thus, unconscious thought is allegedly capable of handling a large amount of information without
requiring attention or cognitive resources.
We agree with Newell & Shanks (N&S) that the unconsciousthought effect, like many others purporting to demonstrate the
capacity of unconscious processes, does not mandate the existence
of a powerful unconscious system that works just like the conscious system, only minus consciousness (Cleeremans &
Jiménez 2002). We also confirm that the effect is difficult to replicate (Waroquier et al. 2009; see also Klein et al. 2012). However,
in contrast to N&S’s position, we have to admit that the results of
the most recent meta-analysis (Strick et al. 2011) support the
existence of the effect, and even more importantly, that it can
be replicated under specified conditions. One of the important
moderators we identified is the difficulty of the distraction task:
The effect is more likely to occur with easy distraction tasks.
Response/Newell & Shanks: Unconscious influences on decision making
Two independent sets of experiments that directly manipulated
the difficulty of the distraction task now provide evidence that
low-demanding distraction improves complex decision making
compared with deliberation, high-demanding distraction, or
immediate choices (Abadie et al. 2013; McMahon et al. 2011).
Similarly, a recent study suggests that low-demanding distraction
improves creativity as well (Baird et al. 2012). Abadie et al. (2013)
also provide evidence that the effect is accompanied by a better
organization of the information conveyed by decision cues, as
shown by an increase in gist memory (see Reyna 2012).
Do such findings confirm the existence of unconscious thought,
or on the contrary, do they support N&S’s claim that evidence for
the intervention of unconscious processes in the utilization of
decision cues is lacking? As it is often the case with interesting
research topics, things are not so simple.
First, we note that neither unconscious-thought theory (UTT)
in its original formulation (Dijksterhuis & Nordgren 2006) nor
the position taken by N&S in the target article suffice to
account for these recent results. Indeed, UTT assumes that
unconscious thought, unlike conscious thought, is not capacitylimited. It is precisely this greater, potentially unlimited capacity
that would explain why people make better decisions after distraction. A straightforward prediction of UTT is therefore that performance should increase as distraction increases, for the more
conscious attention is otherwise engaged, the more unconscious
thought should be able to solve the problem. However, several
studies have shown exactly the opposite: People indeed make
better decisions after low-demanding distraction than after deliberation, but performance after high-demanding distraction is
worse than after low-demanding distraction.
Turning now to N&S’s proposal, we likewise run into an
impasse, for they make the exact opposite – but likewise monotonic – prediction about the effects of distraction on decision
making: The more conscious attention people can dedicate to a
decision-making process, the better the decision should be.
However, this is again not what the most recent studies show.
A solution to this dilemma comes from the important consideration that consciousness (i.e., the ability of being aware of the processes) and attention (i.e., the extent to which cognitive resources
are allocated to process some specific information) are not identical to each other. Several studies have now documented the fact
that the two can be wholly dissociated (Dehaene et al. 2006;
Koch & Tsuchiya 2006; Dijksterhuis & Aarts 2010). A recent
experiment even adopted a clear 2 × 2 design (Watanabe et al.
2011) to show that consciousness (as assessed by reportability)
and attention are subtended by distinct neural correlates.
Based on this distinction, Strick and colleagues (2011) maintained that the processes involved in the unconscious-thought
effect occur outside conscious awareness but conceded that the
term “deliberation without attention” (e.g., target article; Dijksterhuis et al. 2006b) is a misnomer. They thus suggest that the integration of decision cues is unconscious but needs attentional
resources. According to this view, unconscious thought can not
only perform the same functions (e.g., mathematical calculations;
for evidence, see Ric & Muller 2012) as conscious thought but also
is subject to the same constraints (i.e., limited capacity).
However, Strick et al. (2011) do not show any evidence that
decision cues are indeed used unconsciously (e.g., cues utilization
cannot be reported) during low-demanding distraction (e.g.,
while listening to music). Moreover, since the decision goal is
given explicitly before distraction in the paradigm at hand, it is
likely that some decision-relevant processes occur inside conscious
awareness. If, as N&S do, we endorse the null hypothesis that
complex decisions are made consciously (for evidence, see Baumeister et al. 2011), it seems both safe and parsimonious to assume
that integration of decision cues occurs inside conscious awareness
but is modulated by attentional factors. Congruently with Waroquier et al.’s (2010) conclusions, conscious thought may enhance
decision making; however, while a certain amount of attention is
necessary, too much attention may sometimes be detrimental.
Authors’ Response
The primacy of conscious decision making
doi:10.1017/S0140525X13001507
David R. Shanksa and Ben R. Newellb
a
Division of Psychology and Language Sciences, University College London,
London WC1H 0AP, United Kingdom; bSchool of Psychology, University of
New South Wales, Sydney 2052, Australia.
ben.newell@unsw.edu.au
d.shanks@ucl.ac.uk
http://www.ucl.ac.uk/psychlangsci/research/CPB/people/cpb-staff/
d_shanks
http://www2.psy.unsw.edu.au/Users/BNewell/Index.html
Abstract: The target article sought to question the common belief
that our decisions are often biased by unconscious influences.
While many commentators offer additional support for this
perspective, others question our theoretical assumptions,
empirical evaluations, and methodological criteria. We rebut in
particular the starting assumption that all decision making is
unconscious, and that the onus should be on researchers to
prove conscious influences. Further evidence is evaluated in
relation to the core topics we reviewed (multiple-cue judgment,
deliberation without attention, and decisions under uncertainty),
as well as priming effects. We reiterate a key conclusion from
the target article, namely, that it now seems to be generally
accepted that awareness should be operationally defined as
reportable knowledge, and that such knowledge can only be
evaluated by careful and thorough probing. We call for future
research to pay heed to the different ways in which awareness
can intervene in decision making (as identified in our lens
model analysis) and to employ suitable methodology in the
assessment of awareness, including the requirements that
awareness assessment must be reliable, relevant, immediate,
and sensitive.
In our target article we proposed that unconscious influences have been ascribed inflated and erroneous explanatory power in theories of decision making. Many of the
commentators agreed with our basic position, noting that
more critical treatment of these issues was welcome and
a debate overdue (Baumeister, Vohs, & Masicampo
[Baumeister et al.]; González-Vallejo, Stewart, Lassiter,
& Weindhardt [González-Vallejo et al.]; Huizenga, van
Duijvenvoorde, van Ravenzwaaij, Wetzels, & Jansen
[Huizenga et al.]; Rakow; Steingroever & Wagenmakers; Waroquier, Abadie, Klein, & Cleeremans
[Waroquier et al.]). However, several other commentators strongly disagreed both with our principal claim and
with a perceived selective review of the relevant literature
(Brooks & Stein; Dijksterhuis, van Knippenberg,
Holland, & Veling [Dijksterhuis et al.]; Evans;
Hassin & Milyavsky; McLaren, Dunn, Lawrence,
Milton, Verbruggen, Stevens, McAndrew, & Yeates
[McLaren et al.]). This clear divergence of opinion confirms that researchers remain divided on the role of consciousness in decision making and emphasizes the
continued need for open discussion. We thank all the commentators, both those generally sympathetic to our analysis
and those more critical, for their thoughtful and constructive remarks.
The structure of our reply mirrors that of the target
article. First, in light of the critiques challenging our
45
theoretical conceptualizations of consciousness and
decision making, in section R1 we clarify what we mean
by a “decision” and by an (un)conscious influence, and
why we argue for the primacy of conscious rather than
unconscious decision making. We then go on to consider
commentators’ challenges (and/or extensions) to the conclusions we drew in the main topic areas we reviewed.
Thus section R2 considers multiple-cue learning; R3, the
unconscious thought paradigm; and R4, the Iowa Gambling
Task (IGT). Section R5 elaborates on the additional paradigms that we briefly reviewed – subliminal priming, blindsight, and primes-to-behavior. In section R6 we address
issues that we did not cover in the original article – in particular the intriguing claim that the context in which
decisions occur can exert unconscious influences (e.g.,
Helzer & Dunning). We conclude in section R7 by
suggesting areas in which further study of the potential
for unconscious influences on decision making could be
particularly informative against the backdrop of current
gaps in our knowledge and understanding.
R1. Defining the consciousness of a decision
Dijksterhuis et al., Evans, and Hassin & Milyavsky all
question, in various ways, why we presume that in the
absence of evidence to the contrary, decision making is a
conscious process. These commentators all appear to advocate the opposite position of proposing that all decision
making (all of cognition?) is unconscious, and that the
onus should be on researchers to provide evidence of conscious influences. We find this line of argument ill-conceived and ill-defined.
Dijksterhuis et al. and Evans both suggest (incorrectly) that our position forces us to endorse a form of Cartesian dualism in which conscious “mind stuff” has powers
of causation in the brain. But neither explain how unconscious “mind stuff” – that they argue plays such a central
role in cognition – exerts its influence. Simply arguing
that all conscious thought must have unconscious precursors begs the question of where these precursors come
from.
For the avoidance of doubt, we emphasize that our position does not force us to assume that “consciousness freely
hovers in the air” (Dijksterhuis et al.) nor is it remotely
correct to state that “it borders in the mystical (or at least
Cartesian dualism) to think of consciousness as some kind
of ‘mind stuff’ that has powers of causation” (Evans).
Claiming that all or most decisions have conscious precursors does not force us to embrace dualism or abandon
materialism. We assume that all mental states are brain
states. Software states in computers are equivalent to, or
realized by, hardware states, yet it is still perfectly meaningful to say that a line of software code caused the printer to
start working. Likewise, by any of the usual standards for
judging what a cause is, consciously reportable mental processes cause behavior. This is not a dualistic claim – it is
perfectly consistent with materialism. To say that report
X caused behavior Y means, for example, that Y counterfactually would not have occurred if X had been absent.
A good example is the relationship between conditioning
and awareness. In experiments on this topic, independent
variables are employed to manipulate awareness levels,
with conditioned responding as the dependent variable.
46
Meeting Uhlmann’s criterion, numerous studies demonstrate covariation between reportable awareness and conditioning (Lovibond & Shanks 2002), such that in the
absence of awareness, conditioned responding is absent.
Baumeister et al. make the excellent point that “by the
logic of experimental design, such studies prove causation.”
No one can seriously doubt that such inferences are valid.
Reportable mental states correlate with behavior (my
belief that light switches cause lights to come on correlates
with my pressing the light switch); behaviors are prevented
when the relevant mental state is absent (when I don’t
believe that pressing the light switch will cause the light
to come on – it’s been disconnected – I don’t press the
light switch); and interventions on those states cause or
prevent the behavior (you telling me that you’ve disconnected the light switch will stop me believing that pressing
will cause the light to come on and will stop me pressing the
light switch). No such commonsense case can be made for
unconscious states playing a causal role in behavior. There
are no clear-cut and agreed instances in which an unreportable state causes a behavior that would not have occurred
in the absence of that state. As the target article argues, it
has not even been unequivocally proven that there are
any unreportable states that cause behavior. And as Baumeister et al. say, “Does anyone seriously think that a student
could pass a college math test without conscious thought?”
Evans argues that we have conflated the System or Type
1 versus System or Type 2 distinction with unconscious
versus conscious. We acknowledge that consciousness is
but one aspect by which the proposed different modes of
thinking can be distinguished, and that for at least some
authors it is not the primary one. Nonetheless, the particular instantiation of dual-process theorizing we focused on in
the article (Usher et al. 2011) did use awareness of the
stages of processing as a key defining feature of the difference between System 1 and System 2 (as illustrated in the
quotation we took from that article). Appeals to other
defining features, such as involvement of working
memory in System 2 but not System 1 (Evans), are also
fraught because of the difficulties inherent in interpreting
“dissociations” (e.g., Newell & Dunn 2008), and because
there have been repeated demonstrations of the necessity
of working memory for tasks that are purportedly under
System 1 control (e.g., Lewandowsky et al. 2012; Newell
et al. 2007a; 2010). In our view discussion about what
kinds of processes should and should not be included “in”
System 1 and System 2 is exactly the kind of hair-splitting
and unproductive debate that appears to handicap the
development of dual-process theories. In this regard we
agree with Keren and Schul (2009) that “two-system
models currently provide little scientific advance” (p. 533).
Ogilvie & Carruthers contend that even though individuals may be able to give informative reports about
their decision processes, this provides no guarantee that
these reports accurately reflect knowledge that was both
conscious and causal at the time the decision was made.
In relation to the IGT, for instance, the accurate ratings
given by Maia and McClelland’s (2004) participants may
have been based on their affective reactions to the decks,
and not at all causal in regard to deck selection choices.
We commented on this possibility in the target article
(sect. 4.2) and cited some of the extensive evidence
against it in the context of the IGT. But we acknowledge
the more general points that (a) many awareness
assessments probe post facto knowledge that may have
been constructed after the decision itself, and (b) even
for knowledge assessed contiguously with a decision, it is
a challenge to establish that such knowledge was causal in
relation to the decision. One general strategy is to ask
whether verbal reports correlate better with choices than
do affective reactions or somatic markers; Gutbrod et al.’s
(2006) data suggest this is highly likely, though more
research is needed on this issue. To that extent, it is plausible to attribute a causal role to conscious knowledge.
Several commentators (Bernacer, Balderas, Martinez-Valbuena, Pastor, & Murillo [Bernacer et al.];
Hogarth; Srinivasan & Mukherjee) raise the issues of
attention, automaticity, and habitual behavior. We agree
that attention is distinct from awareness and that attention
is required for virtually all decisions. Hogarth expresses surprise that we did not explore the topic of automatic processes in more detail. We do not dispute that the
acquisition of many physical or perceptual-motor skills
involves a period in which people are acutely aware of
their movements, and that when such skills are mastered,
they are executed with very shallow phenomenological
experience. However, just because we allocate very little
attention to and engage in minimal monitoring of the
mental operations involved in performing perceptualmotor skills, it does not follow that such skills are controlled
and executed unconsciously. A relevant example comes
from studies of ball catching. Although this ability is often
highlighted as a paradigmatic case of a skill outside conscious control, detailed analyses of what people know
about the cues they use to decide whether to advance or
retreat to intercept and catch a ball reveal surprisingly
rich and accurate information (e.g., Reed et al. 2010).
Thus while we agree with Hogarth that it can be difficult
to prove or disprove the role of unconscious influences in
such skills, those studies that have tried to do so provide
evidence that falls far short of demonstrating independence
from conscious control.
Bernacer et al. suggest that habits are a distinct form of
behavior characterized by unconscious triggering alongside
conscious monitoring. Thus consciousness can reclaim
control of behavior when a difficulty arises. We are not convinced that it is meaningful to say that habits involve any
decisions at all. A pianist is not deciding to play each
note, and as evidence for this we would point to the fact
that true habits run on independently of their consequences: They are “pushed” by the eliciting cues rather
than “pulled” toward a goal. Dickinson (1985) has shown
that habitual responses (such a lever pressing by a hungry
rat) continue even if their outcomes (food pellets) have
been devalued (e.g., by being paired with poison). A defining characteristic of a decision is that it involves the weighing of different outcomes, which is precisely what is absent
in habitual behavior.
In the target article we employed a lens model analysis to
explicate the ways in which unconscious influences on behavior might be realized. An elegant redescription of the
issue is provided by Sher & Winkielman, who frame it
in terms of the relationship between cognition and metacognition. We would certainly not want to take up the challenge they put to us of proposing a cognitive architecture in
which cognition and metacognition are inseparable.
However, we stress that their cognition/metacognition
view does not align with our own conception. We do not
believe that a prominent place in the human cognitive
architecture needs to be assigned to mental states that
may (or may not) become the object of other, metacognitive, states. Instead, we believe that awareness and reportability are intrinsic properties of many mental states. States
become reportable not because other states pick them up
and move them to the consciousness box, but because
reportability is part of (or an affordance of) what they are
as states in the first place. How much hinges on this conceptual disagreement we leave for others to judge. Our
principal claim is an empirical one, namely, that the coupling between cognition and metacognition, if that is how
one wishes to frame it, is far tighter than many have
assumed, although we certainly do not deny the theoretical
possibility that they can be dissociated. Sher & Winkielman
offer the distinction between procedural and declarative
knowledge as an empirical example, the former being the
“cognitive” part and the latter the “metacognitive” part.
We have analyzed this distinction in considerable depth
elsewhere (Berry et al. 2012) and maintain that the evidence does not support the view that people can only
“declare” a subset of their procedural knowledge. Last,
we agree wholeheartedly with the point Sher & Winkielman make about the incentives in place that might
induce researchers to make Type I errors in their observations of the behavior of interest but Type II errors concerning their observations about participants’ reports
about those behaviors.
Ingram & Prochownik quote from Haidt (2007) concerning fast and automatic moral intuitions “in which an
evaluative feeling of good-bad or like-dislike … appears
in consciousness without any awareness of having gone
through steps of search, weighing evidence, or inferring a
conclusion,” to which we reply, what’s the evidence that
these intuitive responses went through such steps?
Perhaps they were based on one-reason decision making?
We also suggest that Ingram & Prochownik are muddled
in two further respects. First, we certainly do not (as they
imply) confuse awareness of stimuli with awareness of
their influence: The lens model framework incorporates
this distinction very clearly. Second, they misinterpret the
proximal–distal distinction so as to effectively equate a forgotten or neglected distal cue with an unconscious influence. These are quite different things. Our argument is
that forgotten distal cues (e.g., Mother always told me
that spinach was good for me) are irrelevant to understanding decision making if they causally triggered a chain of
events that eventuates in a reportable proximal cue (the
current belief that spinach is healthy).
Velmans asks us to consider distinct ways in which a
process might be conscious and suggests that adopting a
broader perspective leads to the conclusion that evidence
for unconscious mental processes is “ubiquitous.” To illustrate his point, Velmans considers the phenomenological
experience of reading the sentence: If we don’t increase
the dustmen’s wages, they will refuse to take the refuse.
He argues that the syntactic and semantic processing
required to assess meaning and assign appropriate stress
to “refuse” in the two instances in which it appears must
occur outside awareness. This conclusion, however,
appears to be at odds with our (and others’, based on an
informal survey) phenomenological experience: Encountering the second instance of “refuse” provokes hesitation
in readers precisely because there is an awareness of the
47
need to correct an initial temptation to pronounce it using
the same stress as in the first instance. The correction
appears to be a clear instance of “conscious control” over
our behavior: An action was initiated (i.e., saying
“refuse”), but we “selected” an alternative (i.e., saying
“refuse”). Many of us could probably also (correctly) introspect that the inappropriate pronunciation was triggered on
the second encounter because we were primed by the
initial, alternative pronunciation.
In the target article we made it clear that we do not consider it useful to ask whether, for example, area V5’s computation of motion is or is not conscious because we view
consciousness as a property of individuals. Thus Velmans’s
claim that because we are “not conscious of the complex
input analyses” involved in reading text on a page, such processes must occur outside conscious control is, we would
argue, a category mistake (Ryle 1949). In contrast, it is perfectly reasonable to ask whether an individual is conscious
of hesitation in reading sentences containing heteronyms.
R2. Multiple-cue judgment: Challenges and
extensions
Rakow and González-Vallejo et al. make the very important point that the validity of self-insight measures is dependent on assumptions about the model underlying
judgment. We noted this issue briefly in the target article
(sect. 2.4) and agree that judges may often use other
models such as similarity to exemplars or sequential heuristics in their judgments. We welcome Rakow’s concept
of “double-model recovery” and think it could be used
very profitably in future research on policy capturing and
self-insight. Both commentaries reinforce the point that
an inappropriate approach to modeling the judge’s implicit
policy may lead us incorrectly to misattribute poor selfinsight.
R3. Deliberation without attention: Challenges and
extensions
We note that of the commentaries which discussed unconscious-thought theory (UTT) and deliberation without
attention (DWA), all except one agreed with our general
conclusion that such studies fail to provide unequivocal evidence for the involvement of active unconscious processes
(González-Vallejo et al.; Huizenga et al.; Srinivasan &
Mukherjee; Waroquier et al.). Even those who disagreed with almost everything else in our article agreed
that the claims made for the powers of unconscious
thought are too strong (Evans).
The exception was Dijksterhuis et al., who criticize us
for (among other things) “cherry picking” the studies we
reviewed in regard to the DWA effect. Our review was
necessarily selective, bearing in mind the burgeoning literature on this topic, and our inclusion criterion was firmly
based on discussing the strongest evidence, whether for
or against unconscious influences. Dijksterhuis et al. refer
to their recent meta-analysis (Strick et al. 2011), which
they claim now allows the unconscious thought effect to
be replicated with greater ease. What happens when all
the moderators of the effect that are identified in this
meta-analysis are set to the values most conducive to
48
obtaining it? Nieuwenstein and van Rijn (2012) provide
the answer to this question: It is not obtained. These
authors carefully set up a DWA experiment so as to optimize the likelihood of a benefit from unconscious
thought, but they had no success whatsoever. For
example, they ensured that pictorial information was presented with the choice options together with a relatively
short presentation time, factors identified in the metaanalysis as being important moderators. Their results
suggest that the Strick et al. (2011) meta-analysis should
be treated with considerable caution.
An additional moderator adhered to (in vain) by Nieuwenstein and van Rijn was the use of a moderately difficult
distractor task in the unconscious thought condition. Waroquier et al. discuss recent findings suggesting that DWA
effects are strongest when “low-effort” distractor tasks are
used (Abadie et al. 2013; McMahon et al. 2011). As an
initial comment, we note that only the latter study includes
an experiment that satisfies our (non-arbitrary) criteria for
demonstrating a DWA effect (see target article sect. 3.1),
and that experiment (McMahon et al. 2011, Experiment 1)
failed to counterbalance the presentation order of different
choice options, leading to the possibility that the DWA
advantage was simply a result of a recency bias (cf. Newell
et al. 2009). (We also note that the mode of thought effect
in their second experiment – which did not include an
immediate thought condition – failed to reach conventional
levels of statistical significance.)
The results of the Abadie et al. (2013) study are intriguing, and we welcome further replications using the modified procedure that they adopted in their experiment.
However, the more general point made by Waroquier
et al. – that “too much attention may sometimes be detrimental” is not at odds with our conclusions. Contrary to
Waroquier et al.’s claim, we do not propose a monotonic
“more conscious attention=better decision making” view
(see sect. 6.2 of the target article). Indeed, one of us
(Shanks 2006) was quick to point out that the Dijksterhuis
et al. (2006b) result may well have been due to the detrimental effect of inappropriate conscious thinking rather
than any advantage of unconscious thinking (cf., Mamede
et al. 2010; Payne et al. 2008). We agree that some of
these issues can be clarified by demarcating the differences
between attention and awareness (cf. Srinivasan &
Mukherjee), but simply suggesting that the term “deliberation without attention” could be replaced with “deliberation without consciousness” (see Strick et al. 2011) is
not going to help matters. In our view, and it seems that
of many commentators, evidence of active processing
occurring “outside” conscious awareness in this paradigm
is lacking.
In this regard we concur with Huizenga et al., who
make the insightful point that the unconscious thought
paradigm is ill-suited to test claims about the superiority
of different modes of decision making (cf. Srinivasan &
Mukherjee; Newell et al. 2009). The strategy-convergence
issue raised by Huizenga et al. is an important one to tackle
if we are to obtain clear evidence about the purported abilities of unconscious thought. Progress is already being
made in this regard, as noted by Huizenga et al., and to
their list we add another recent study by Pachur and
Forer (2013) that sheds light on the use of different
decision strategies (e.g., LEX, WADD, EQW) following
different modes of thought. Pachur and Forer find a
slight (and statistically nonsignificant) tendency for participants in an unconscious thought condition to use a compensatory equal-weights strategy (EQW) more than those
in a conscious thought condition, but find no difference
in the use of a weighted-additive strategy (WADD) across
conditions. This latter finding is clearly contrary to the
weighting principle of UTT (Dijksterhuis & Nordgren
2006), which states that unconscious thought leads to
more efficient and accurate weighting of attribute values.
On a related point, we were also somewhat surprised by
Dijksterhuis et al.’s statement that “Obviously, participants are capable of generating post hoc weights that
justify their previous choice.” While we agree, we thought
the key claim was that choices following unconscious
thought would be more consistent with these weights
than those following conscious and immediate thought
(e.g., Dijksterhuis 2004). In line with the conclusions of
González-Vallejo et al., we see little evidence in the literature to support this claim.
In a somewhat related vein, Uhlmann questions our
interpretation of research on “reasons analyses,” arguing
that such effects are consistent with people lacking conscious introspective access into the “true” bases for their
attitudes and subsequent choices (e.g., Wilson & Schooler
1991). The key feature of these studies is that participants
who are invited to provide reasons to support their choices
end up making objectively poorer decisions, and sometimes
exhibit greater post-choice regret, than those who make
“unexamined” choices. While such studies support the
idea that preferences are constructed, labile, and influenced (sometimes detrimentally) by deliberation, we fail
to see why they force the conclusion that some influences
on choice lie outside awareness. Both sorts of choice –
those made intuitively and ones accompanied by an analysis
of reasons – are, we contend, accompanied by awareness of
the proximal basis for that choice. The fact that this proximal basis might not be the same in the two cases does not
imply that the unexamined choice was mediated via an
unconscious process.
R4. Iowa Gambling Task: Challenges and
extensions
We argued that participants are able to learn to make
advantageous choices in the IGT but concurrently
acquire considerable levels of awareness and insight into
the payoff structure of the decks and of the optimal
decision-making strategy. Steingroever & Wagenmakers
argue that in one important respect our conclusion is incorrect: Participants do not learn to discriminate the good
from the bad decks at all (Huizenga et al. make a
similar point). It must be emphasized, however, that
although Steingroever & Wagenmakers dispute our analysis, their viewpoint does not challenge our general conclusion about the role of awareness in decision making:
If, as they claim, there is minimal discrimination in the
IGT, then it also provides no evidence of unconscious influences on decision making.
It is important to note that the conclusions of Steingroever & Wagenmakers may be overly strong, in two
respects. First, their statement that there is “a lack of
both conscious and unconscious knowledge in this task” is
contradicted by their own results, which show that
participants learn a great deal about the decks – albeit
about their associated loss frequencies rather than longrun payoffs. In principle, this loss-frequency learning
could be unconscious.
Second, while we agree with them that participants in
the IGT often show a prominent frequency-of-losses
effect (a result we ourselves have obtained), this does not
mean that they fail to show discrimination between good
and bad decks. In our own studies (Konstantinidis &
Shanks 2013), such discrimination has invariably been statistically significant. We suspect that one or more methodological factors to do with the payoff schedule or the level of
performance-related reward or indeed the inclusion of
awareness measures may account for this difference,
though clearly more work on this issue is called for. But
we reiterate that whether or not participants can discriminate good from bad decks in the IGT, Steingroever &
Wagenmakers agree with us that the IGT provides
minimal evidence for unconscious influences.
In a related comment, McLaren et al. suggest that
some studies on the IGT (and variants thereof) that we
omitted from our review do show evidence for unconscious
influences. However, McLaren et al. themselves note that
one of these studies (Guillaume et al. 2009) adopted a
less than ideal method for indexing awareness. We share
this reservation. Guillaume et al. found that explicit knowledge and differential skin conductance response (SCR)
magnitude can be uncorrelated. These researchers presented their participants with a standard 100-trial IGT,
measuring SCRs concurrently with card selections. Awareness was only assessed at the end of the task, and Guillaume
et al. used responses to the awareness questions to classify
participants as having no awareness, partial awareness (conscious knowledge of which decks were good or bad), or
complete awareness (knowledge of the relative payoffs of
the decks). Whereas participants classified as having no
awareness performed at chance on the task, higher levels
of awareness were associated with increasing proportions
of selections from the good decks. Thus awareness correlated with card selections. Likewise, Guillaume et al.
found that the extent to which SCRs differed in anticipation of bad versus good deck selections correlated with
choice behavior. Yet awareness was not correlated with
differential anticipatory SCRs.
While Guillaume et al. (2009) speculated that such a
finding is consistent with awareness and somatic markers
having independent influences on decision making, they
also acknowledged that the nonsignificant correlation
(reported as reaching p = 0.1) could simply be the result
of low statistical power. We would add to this that their
awareness classification was less than ideal as it was presented only once at the end of the task (raising problems
of lack of immediacy) and did not include any questions
requiring numerical estimates. Instead, the questions
required very coarse-grained responses (e.g., “suppose
you select 10 new cards from the deck, will you on
average win or lose money?”). Since other studies show
a gradual development of differential anticipatory SCRs
(Gutbrod et al. 2006) and a gradual development of differential awareness (Bowman et al. 2005; Cella et al. 2007;
Evans et al. 2005), it seems likely that a positive relationship between awareness and SCR differentiation would be
observed if the former were measured more sensitively
and immediately.
49
A further study is cited by McLaren et al. as providing
evidence for unconscious influences in an IGT-like task. In
this study (Dunn et al. 2011), participants in one group
were probed by the Maia and McClelland (2004) awareness
questions. For another group, decision making (deck selections) was related to bodily signals (i.e., somatic markers)
based on electrodermal responses and heart rate, with
the latter two measures being combined into an index of
“bodily differentiation,” which assessed for each participant
his or her bodily reaction to the good versus the bad decks.
Dunn et al. also measured these participants’ sensitivity to
their own heart rate. The main and highly intriguing finding
was that in those individuals who showed high sensitivity to
their own heart rate, deck selections were correlated with
bodily differentiation, whereas this was not the case for
those who showed poor sensitivity. This pattern suggests
that bodily signals play an important role in decision
making, but only to the extent that they are accurately perceived. Although this is an impressive finding, there are two
reasons why it is more consistent with a primary role for
conscious processes in decision making than with a causal
role for somatic markers. First, sensitivity to heart rate
was assessed via a consciously reportable measure,
namely, participants’ accuracy in reporting the number of
beats in a specified time interval. Second, in the group of
participants who were administered awareness probes,
above-chance levels of awareness entirely in line with
those reported by Maia and McClelland (2004) were
observed. Hence awareness was, at the very least, strongly
correlated with the variables assumed to be causally related
to deck selections. A promising avenue for future research
would be to collect all of the relevant data within a single
group of participants to allow analytic techniques such as
structural equation modeling to be brought to bear to
untangle the causal pathways.
McLaren et al. also refer to research on the relationship
between rules and the “peak-shift” effect, which demonstrates striking qualitative differences in patterns of behavior between individuals who can versus those who cannot
report a categorization rule. We acknowledge that such
effects, although reliant on retrospective reports, provide
impressive evidence for unconscious influences. Future
studies employing online awareness assessments would be
very valuable.
R5. Subliminal priming, primes-to-behavior, and
blindsight: Challenges and extensions
In our view, the points of disagreement highlighted by
Finkbeiner & Coltheart and Snodgrass, Shevrin, &
Abelson (Snodgrass et al.) are vastly overshadowed by
the common ground we share with them. These commentators review in detail some of the factors that might lead
unwary researchers to draw erroneous conclusions from
subliminal priming experiments, such as using inappropriate awareness discriminations (e.g., prime categorization),
response biases, task difficulty, and null sensitivity. As Finkbeiner & Coltheart explain, the latter problem alone
renders virtually all work conducted within the null hypothesis significance-testing framework uninterpretable. Similarly, on the basis of the doubts they raise over the use of
identification and categorization tasks to assess prime
awareness, Snodgrass et al. conclude that virtually all
50
recent studies of “subliminal investigations of cognitive
control processes … do not conclusively rule out conscious
partial identification.”
We thoroughly applaud the careful methods employed in
the impressive studies Finkbeiner & Coltheart and Snodgrass et al. describe, which appear convincingly to demonstrate subliminal effects (e.g., Finkbeiner 2011) – and we
very much hope that future explorations expand these
research programs into more mainstream decision-making
tasks – and evaluate priming effects over considerably
longer time intervals than a few tens of milliseconds. But
the most important point is that almost all decision-making
research reported in the past few years using subliminal
priming methods has fallen far short of the methodological
requirements described by Finkbeiner & Coltheart and
Snodgrass et al., and until this is recognized, inferences
about unconscious influences must remain in doubt.
We imagine that Finkbeiner & Coltheart and Snodgrass et al. will view Hassin & Milyavsky’s and Brooks
& Stein’s conclusions in much the way that we do,
namely, as considerably overestimating the implications
of recent research and underestimating the viability of
alternative explanations. To give just one example, Hassin
& Milyavsky refer to research using continuous flash suppression by Sklar et al. (2012), which appears to show
that reading and doing arithmetic can be achieved unconsciously. Yet by their own awareness tests, and putting
aside issues such as task difficulty and null sensitivity in
the awareness check, the majority of Sklar et al.’s participants were conscious (i.e., performed above chance in a
forced-choice discrimination). Sklar et al. only obtained evidence of unconscious processing by eliminating participants post hoc who performed above chance on the
awareness test. As we have argued at length elsewhere
(Shanks & Berry 2012), this introduces a statistical artifact
(regression to the mean) that renders the apparent evidence of unconscious processing almost meaningless. At
the very least, these experiments need to be done in such
a way that each subject is rendered categorically unconscious of the prime, rather than it being left to individual
perceptual variability. Brooks & Stein describe subliminal
fMRI studies that purportedly demonstrate activation of
neural systems involved in emotion and memory such as
the amygdala and hippocampus. This is undoubtedly an
important research topic, but until due attention is
devoted to the methodological issues described by Finkbeiner & Coltheart and Snodgrass et al., interpretation must
remain uncertain.
Persaud & McLeod describe data from a binary exclusion task in which participants see a briefly presented letter
(“b” or “h”) on each trial and are asked simply to report the
letter that was not shown. Their experiments with this task
(e.g., Persaud & McLeod 2007) reveal that when the
stimuli are presented for 15 ms, participants follow the
instructions without undue difficulty and tend to respond
“h” when “b” is presented and vice versa. However, at
shorter presentation durations (5–10 ms) a striking reversal
is found, whereby participants tend to report the stimulus
that was presented, counter to the instructions. Persaud
& McLeod argue that this must be an unconscious influence on responding because if information about the stimulus was consciously represented, participants would follow
instructions and respond with the identity of the stimulus
not shown.
We acknowledge the elegant simplicity of this demonstration and urge researchers to explore it further (see
Table R1). Other studies using this basic task have not
obtained the same result (Fisk & Haase 2006; 2007), so
its basis and boundary conditions require further exploration. We also note the peculiarly contradictory position
that Persaud & McLeod inevitably find themselves in
regarding their definition of unconscious perception. In
their studies, they found that at very short presentation durations participants could not make the correct exclusion
response, and instead reported the identity of the presented letter. Persaud & McLeod take this to be an unconscious effect. At the same presentation duration, however,
participants successfully reported the identity of the presented letter when explicitly instructed to do so (inclusion
instructions – i.e., report “b” when “b” is present). The
latter would, of course, normally be taken as direct evidence of conscious, not unconscious, processing.
Taking a similar line, Uhlmann cites studies in which
priming effects from unobtrusive stimuli attenuate or
even reverse when participants become aware of the stimulus. But there are many reasons why a change in cognitive
state might modulate priming, even for conscious primes
(Higham & Vokey 2000). From the fact that altered
levels of awareness (e.g., from weak to strong) may
reduce priming effects, it does not follow that priming
can occur unconsciously.
The data surrounding blindsight are extensive and
complex, but the idea that blindsight is little more than
degraded conscious vision has proven extremely difficult
Table R1. Suggested studies where further research could address
major outstanding questions.
Primary citation
Issue to be addressed
Dunn et al. (2011)
Measuring awareness, bodily
differentiation (somatic
markers), sensitivity to bodily
signals, and payoff knowledge
within subjects in variants of
the IGT
Subliminal priming applied to
more typical decision-making
tasks and over longer time
intervals
Strategy classification in the
UTT paradigm to identify if/
how decisions change
following distraction
Peak-shift and verbalizable
rules employing online
awareness assessments
Use of a new awareness
instrument in blindsight and
normal vision
Binary exclusion task
Unobtrusive priming
techniques such as using eyetracking to “prompt”
decisions
Finkbeiner (2011)
Huizenga et al.
McLaren et al.
Overgaard et al. (2008)
Persaud & McLeod (2007)
Richardson et al. (2009)
to refute. In the target article we reviewed Overgaard’s
(2011) findings that when individuals with blindsight are
asked to report whether they have “no experience,” a
“brief glimpse,” or an “almost clear experience” of a stimulus, correlations are observed between awareness and discrimination accuracy. Brogaard, Marlow, & Rice
(Brogaard et al.) object that such correlations do not
prove that the reports are indicative of visual awareness,
and could instead reflect “awareness associated with the
higher-order predictive act,” that is, awareness of being
able to make a judgment. We do not see the force of this
objection. Whichever construal is correct, it would
remain the case that in the absence of awareness (either
visual or judgmental), discrimination would be at chance.
Dijksterhuis et al. found it “mystifying” that we did not
discuss a study by Soon et al. (2008). In a modern neuroimaging adaptation of the Libet task, Soon et al. presented
their participants with a stream of letters (1 every 500 ms)
and asked them to make a left or right button-press at a
freely chosen time point. Participants then reported the
letter that had been on the display at the moment they
felt they formed their conscious choice. Using advanced
methods for decoding neural activity, Soon et al. found
that several seconds before the choice was made, and
long before it was conscious, two brain regions (frontopolar
and precuneus/posterior cingulate) contained information
that predicted that choice.
Soon et al. (2008) concluded from these findings that
there is a significant contribution of unconscious processes
to decision making. But this conclusion rests on adopting
the assumption that participants go instantaneously from
a state of no bias (i.e., 50:50 right/left) to a state in which
they have sufficient bias to commit to a response. It is
surely the case that the process of forming a decision
takes time. Suppose that a threshold degree of bias or preference (100:0) is required before a participant makes a
voluntary movement of the left or right hand. Then the
accumulation of bias prior to reaching this threshold
could be entirely conscious and neurally measurable for
tens or hundreds of milliseconds, even before it compels
the button-press. When individuals report the time at
which they consciously made their decision, perhaps they
(perfectly reasonably) report the point at which their bias
reached, say, 70:30, rather than the point it first drifted
away from 50:50. The key point is that the threshold for
detecting neural activity does not have to be the same as
the threshold for reporting a state of awareness.
The notion of information accumulation is more than
just a vague possibility. Numerous theories of decision
making have developed precise formalizations of the
accumulation idea. For example, random walk models conceive of decision making in terms of time-steps during
which evidence moves in one direction or another by
small amounts. When the total evidence reaches a
threshold, a choice is made. Although they have not
usually considered whether accumulated information is
conscious or unconscious, these models have been very
successful in explaining response time distributions and
other aspects of choice (e.g., Newell & Lee 2011). Soon
et al.’s (2008) findings provide important evidence about
the high-level brain structures involved in the development
of decisions, but they seem entirely consistent with the idea
that consciousness is a necessary component of, and precursor to, our choices.
51
R6. Additional perspectives: Context, causal fields,
and emotions
Hogarth makes the interesting point that mismatches may
occur between verbal reports about causal influences and
the reality of those influences as a result of experimenters
and participants adopting different perspectives on the
“causal field.” In Hogarth’s example, a couple enjoying a
romantic meal in a restaurant might deny that the level of
lighting influenced their behavior, whereas an experimenter able to compare behavior between-subjects in conditions of low or high lighting might conclude that lighting
level did influence behavior. Such differences in conceptualization of the causal field might lead to erroneous conclusions, as the two people are surely right that (from
their perspective) they only experienced one level of lighting and therefore do not have the evidence necessary to
assign it a causal role.
A related issue is raised by Hytönen and Helzer &
Dunning. It is well-known that formally equivalent decision
problems can lead to different decisions depending on the
way they are framed. For instance, people may prefer
“75% lean” ground beef to “25% fat” beef. Hytönen’s
view is that such effects result from unconscious emotional
signals arising from System 1, which may in some circumstances be suppressed by conscious System 2 control processes. Hytönen describes neuroscientific evidence that
she takes to provide support for this two-system view. We,
in contrast, find this explanation both unparsimonious and
ill-specified. A more plausible explanation is that a typical
person does not necessarily believe her interlocutor is
saying quite the same thing when he says “75% lean” and
“25% fat,” or when he says “the glass is half full” and “the
glass is half empty,” and that is why she may behave differently (Sher & McKenzie 2006; 2011). Helzer & Dunning,
commenting on the same sort of framing effect, suggest
that people are likely to have poor insight into the impact
of context variables on their decisions. We disagree. “Information leakage” studies (Sher & McKenzie 2006; 2011)
provide evidence that people are sensitive to the implications of the chosen frame.
Helzer & Dunning describe evidence that, for example,
many more people will agree that they would hypothetically
dance in front of an audience for a small amount of money
than will actually do so when faced with the same choice
for real. This seems to suggest a lack of awareness of how
a future emotional state (embarrassment) would influence
behavior. Similarly, being sated as opposed to hungry
decreases the likelihood of choosing a high-calorie food to
eat at a future time point, as if people are not always fully
aware of how their future bodily states will affect their preferences. These examples of lack of insight are striking, but
we do not see that they in any way demonstrate unconscious
influences on behavior. Rather, they are consistent with a
much simpler explanation, namely, that imagined cues or
contexts are often weaker than the real thing. When I contemplate a future time point at which I will have to dance
in front of an audience, my imagination fails adequately to
represent how embarrassing the situation will be. Both the
hypothetical decision and the real one are based on conscious influences and cues. Where they differ is in terms
of the cues themselves.
Still on the theme of the contexts in which decisions are
made, Antony argues that many influential studies
52
appearing to demonstrate unconscious influences in
decision making involve asking participants to introspect
in “degraded” conditions in which they have no reasons
for their decisions. We fully endorse Antony’s point that
it is often inappropriate to assume that how we solve problems in normal conditions is similar to how we solve
them in degraded conditions. In both cases, a full characterization of the decision process is required before questions can be meaningfully asked about the individual’s
awareness. We are less convinced that people “confabulate”
in degraded conditions. We argued that in Nisbett and
Wilson’s (1977) stockings example, participants may have
employed a sequential comparison rule such as “if the
next one is as good as the previous one, go with the next
one.” Antony wonders why participants did not report
that they were using this rule. We contend that (a) the
“rule” is as much in the environment as in participants’
heads, in the sense that the situation offers behavioral affordances including left-to-right choice, and in any case (b)
they did report the crucial component of the rule,
namely, that it involved a comparison of items in terms of
their quality (“…as good as…”).
There is evidence that the relationship between preferences and choices is bidirectional. That is, in addition to
preferences influencing the choices one makes, choices
seem to retrospectively alter preferences. Coppin
reviews evidence on this issue, with a particular focus on
whether the latter effect is modulated by awareness. As
Coppin notes, a considerable body of evidence suggests
that awareness is necessary for choice-induced preference
changes. However, she cites recent studies pointing to
the opposite conclusion. We find the latter evidence
weak. As an example, Sharot et al. (2010) reported evidence for post-decision preference changes but collected
no conscious reports from their participants and made no
claim that the effects they observed were unconsciously
mediated. Coppin et al. (2010) reported similar data for
choices between pairs of odors, although in this case awareness was assessed via a post-choice explicit recognition test.
Their procedure involved initial liking ratings for single
odors, and then choices between pairs of odors; and
finally the individual odors were re-rated, and participants
indicated whether each was new or old. Choice again
affected preferences (chosen odors became more liked
and rejected ones more disliked) and did so even for
odors that were later forgotten. But it is easy to explain
such patterns on the basis of a single, conscious, knowledge
state, as we have shown in regard to classic dissociations
between implicit and explicit memory (see Berry et al.
2012; Shanks & Berry 2012). Post hoc selection of forgotten versus remembered items introduces the same statistical artifact that we highlighted in regard to the Sklar et al.
(2012) subliminal priming data. Much more compelling
would be a demonstration of a preference change across
an entire participant group performing at chance in odor
recognition (e.g., as a result of a delayed test).
We thoroughly concur with Uhlmann and Hahn &
Gawronski that the role of unconscious processes in
tasks like the Implicit Association Test (IAT) has been substantially exaggerated and that when sensitive tests are
employed, implicit and explicit attitudes tend to show
reliable levels of correlation. For instance, Hahn &
Gawronski review evidence showing that asking participants to report their predictions of implicit evaluations
References/Newell & Shanks: Unconscious influences on decision making
(conscious reports) yields stronger correlations with actual
implicit evaluations than is observed when standard explicit
evaluations are compared to implicit ones. Such a pattern
suggests that low implicit–explicit correlations may arise
in the IAT and similar tasks because standard explicit evaluations are not fully valid indicators of awareness.
R7. Conclusions
We reiterate our view that the unconscious has been
afforded an unwarranted degree of explanatory power in
models of cognition. Although it is convenient to think of
our main question (are there unconscious influences on
decision making?) in binary terms, ultimately this question
will inevitably turn out to require a more complex and
nuanced answer than a simple yes or no. In all likelihood
unconscious influences will be established in certain conditions, although it remains to be seen whether it plays a
trivial or a more significant role in these conditions. Our
argument, however, is that (a) the evidence available thus
far falls significantly short of establishing the importance
of such influences, and (b) future research should take
careful heed of the methodological issues that have been
raised. Awareness can only be evaluated using careful
methods. We call for future research to acknowledge the
different ways in which consciousness can be involved in
decision making (as highlighted in our lens model analysis)
and to employ suitable methodology in the measurement of
awareness, including awareness assessments that are
reliable, relevant, immediate, and sensitive.
References
[The letters “a” and “r” before author’s initials stand for target article and
response references, respectively]
Abadie, M. Waroquier, L. & Terrier, P. (2013) Gist memory in the unconscious
thought effect. Psychological Science 25:1253–59. [AD, LW, rDRS]
Acker, F. (2008) New findings on unconscious versus conscious thought in decision
making: Additional empirical data and meta-analysis. Judgment and Decision
Making 3:292–303. [aBRN]
Ackerman, J. M., Nocera, C. C. & Bargh, J. A. (2010) Incidental haptic sensations
influence social judgments and decisions. Science 328:1712–15. [aBRN]
Adair, J. G. & Spinner, B. (1981) Subjects’ access to cognitive processes: Demand
characteristics and verbal report. Journal for the Theory of Social Behaviour
11:31–52. [aBRN]
Alcaro, A. & Panksepp, J. (2011) The SEEKING mind: Primal neuro-affective
substrates for appetitive incentive states and their pathological dynamics in
addictions and depression. Neuroscience Biobehavioral Reviews 35:1805–20.
[SJB]
Allen, V. L. (1965) Effect of extraneous cognitive activity on dissonance reduction.
Psychological Reports 16:1145–51. doi: 10.2466/pr0.1965.16.3v.1145. [GC]
Andrade, E. B. & Ariely, D. (2009) The enduring effect of transient emotions on
decision making. Organizational Behavior and Human Decision Processes
109:1–8. [RMH]
Ariely, D. & Norton, M. I. (2011) From thinking too little to thinking too much: A
continuum of decision making. Wiley Interdisciplinary Reviews: Cognitive
Science 2:39–46. [aBRN]
Arkes, H. R. (1981) Impediments to accurate clinical judgment and possible ways to
minimize their impact. Journal of Consulting and Clinical Psychology 49:323–
30. [aBRN]
Arndt, J., Greenberg, J., Pyszczynski, T. & Solomon, S. (1997) Subliminal exposure to
death-related stimuli increases defense of the cultural worldview. Psychological
Science 8(5):379–85. doi: 10.1111/j.1467-9280.1997.tb00429.x. [RRH]
Ashby, N. J. S., Glöckner, A. & Dickert, S. (2011) Conscious and unconscious
thought in risky choice: Testing the capacity principle and the appropriate
weighting principle of unconscious thought theory. Frontiers in Psychology
2:261. [NS]
Aznarez-Sanado, M., Fernandez-Seara, M. A., Loayza, F. R. & Pastor, M. A. (2013)
Functional asymmetries in early learning during right, left, and bimanual performance in right-handed subjects. Journal of Magnetic Resonance Imaging 37
(3):619–31. [JB]
Azzopardi, P. & Cowey, A. (1997) Is blindsight like normal, near-threshold vision?
Proceedings of the National Academy of Sciences 94:14190–94. [aBRN]
Baars, B. J. (2002) The conscious access hypothesis: Origins and recent evidence.
Trends in Cognitive Sciences 6(1):47–52. [aBRN, RFB]
Baars, B. J. (2005) Global workspace theory of consciousness: Toward a cognitive
neuroscience of human experience. Progress in Brain Research 150:45–53. [SS]
Baars, B. J. & Franklin, S. (2003) How conscious experience and working memory
interact. Trends in Cognitive Sciences 7:166–72. [NS]
Baddeley, A. D. & Hitch, G. (1974) Working memory. In: The psychology of learning
and motivation: Advances in research and theory, ed. G. H. Bower, pp. 47–89.
Academic Press. [RRH]
Baijal, S. & Srinivasan, N. (2009) Types of attention matter for awareness: A study
with color afterimages. Consciousness & Cognition 18:1039–48. [NS]
Baird, B., Smallwood, J., Mrazek, M. D., Kam, J. W. Y., Franklin, M. S. & Schooler,
J. W. (2012) Inspired by distraction mind wandering facilitates creative incubation. Psychological Science 23:1117–22. [LW]
Balcetis, E. & Dunning, D. (2013) Considering the situation: Why people are better
social psychologists than self-psychologists. Self and Identity 12:1–15. [EGH]
Balzer, W. K., Rohrbaugh, J. & Murphy, K. R. (1983) Reliability of actual and predicted judgments across time. Organizational Behavior and Human Performance 32:109–23. [aBRN]
Banse, R., Seise, J. & Zerbes, N. (2001) Implicit attitudes towards homosexuality:
Reliability, validity, and controllability of the IAT. Zeitschrift für Experimentelle
Psychologie 48:145–60. [ELU]
Bargh, J. A. (2006) What have we been priming all these years? On the development,
mechanisms, and ecology of nonconscious social behavior. European Journal of
Social Psychology 36:147–68. [ELU]
Bargh, J. A., Chen, M. & Burrows, L. (1996) Automaticity of social behavior: Direct
effects of trait construct and stereotype activation on action. Journal of Personality and Social Psychology 71:230–44. [aBRN]
Bargh, J. A. & Morsella, E. (2008) The unconscious mind. Perspectives on Psychological Science 3:73–79. [GPDI]
Bargh, J. A., Schwader, K. L., Hailey, S. E., Dyer, R. L. & Boothby, E. J. (2012)
Automaticity in social-cognitive processes. Trends in Cognitive Science 16
(12):593–605. [ELU]
Baron, J. (2008) Thinking & deciding (4th ed). Cambridge University Press. [aBRN]
Baumeister, R. F. & Masicampo, E. J. (2010) Conscious thought is for facilitating
social and cultural interactions: How mental simulations serve the animal–
culture interface. Psychological Review 117(3):945–71. [RFB]
Baumeister, R. F., Masicampo, E. J. & Vohs, K. D. (2011) Do conscious thoughts
cause behavior? Annual Review of Psychology 62:331–61. doi: 10.1146/annurev.
psych.093008.131126. [aBRN, LW, RFB]
Bechara, A., Damasio, A. R., Damasio, H. & Anderson, S. W. (1994) Insensitivity to
future consequences following damage to human prefrontal cortex. Cognition
50(1–3):7–15. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/8039375.
[aBRN, HMH]
Bechara, A., Damasio, H. & Damasio, A. R. (2000) Emotion, decision making and
the orbitofrontal cortex. Cerebral Cortex 10:295–307. [aBRN, RO]
Bechara, A., Damasio, H., Tranel, D. & Damasio, A. R. (1997) Deciding advantageously before knowing the advantageous strategy. Science 275:1293–95.
[aBRN]
Bechara, A., Tranel, D., Damasio, H. & Damasio, A. R. (1996) Failure to respond
autonomically to anticipated future outcomes following damage to prefrontal
cortex. Cerebral Cortex 6:215–25. [aBRN]
Beckstead, J. W. (2007) A note on determining the number of cues used in judgment
analysis studies: The issue of type II error. Judgment and Decision Making
2:317–25. [aBRN, TR]
Bem, J. D. (1972) Self-perception theory. In: Advances in experimental social psychology, vol. 6, ed. L. Berkowitz, pp. 1–62. Academic Press. [CGV]
Benjamin, L. T., Cavell, T. A. & Shallenberger, W. R. (1984) Staying with initial
answers on objective tests: Is it a myth? Teaching of Psychology 11:133–41.
[aBRN]
Benson, B. L., Anguera, J. A. & Seidler, R. D. (2011) A spatial explicit strategy
reduces error but interferes with sensorimotor adaptation. Journal of Neurophysiology 105:2843–51. [aBRN]
Bermeitinger, C., Goelz, R., Johr, N., Neumann, M., Ecker, U. K. H. & Doerr, R.
(2009) The hidden persuaders break into the tired brain. Journal of Experimental Social Psychology 45(2):320–26. doi: 10.1016/j.jesp.2008.10.001.
[RRH]
Bernacer, J. & Gimenez-Amaya, J. M. (2012) On habit learning in neuroscience and
free will. In: Is science compatible with free will?, ed. A. Suarez & P. Adams, pp.
177–93. Springer. [JB]
53
Berry, C. J., Shanks, D. R., Speekenbrink, M. & Henson, R. N. A. (2012) Models of
recognition, repetition priming, and fluency: Exploring a new framework.
Psychological Review 119:40–79. [rDRS]
Bhalla, M. & Proffitt, D. R. (1999) Visual-motor recalibration in geographical slant
perception. Journal of Experimental Psychology: Human Perception and Performance 25:1076–96. [aBRN]
Bijleveld, E., Custers, R. & Aarts, H. (2009) The unconscious eye opener: Pupil
dilation reveals strategic recruitment of resources upon presentation of subliminal reward cues. Psychological Science 20(11):1313–15. doi: 10.1111/j.14679280.2009.02443.x. [RRH]
Bishop, M. A. & Trout, J. D. (2005) Epistemology and the psychology of human
judgment. Oxford University Press. [CGV]
Blackburn, S. (1986) Finding psychology. The Philosophical Quarterly (Special
Issue: Mind, Causation and Action) 36(143):111–22. [LA]
Blair, I. (2001) Implicit stereotypes and prejudice. In: Cognitive social psychology,
ed. G. B. Moskowitz, pp. 359–74. Erlbaum. [AH]
Blair, I. V., Judd, C. M. & Chapleau, K. M. (2004a) The influence of Afrocentric
facial features in criminal sentencing. Psychological Science 15:674–79. [aBRN]
Blair, I. V., Judd, C. M. & Fallman, J. L. (2004b) The automaticity of race and
Afrocentric facial features in social judgments. Journal of Personality and Social
Psychology 87:763–78. [aBRN]
Block, N. (2007) Consciousness, accessibility, and the mesh between psychology and
neuroscience. Behavioral and Brain Sciences 30:481–99. [aBRN]
Bos, M. W., Dijksterhuis, A. & van Baaren, R. B. (2011) The benefits of “sleeping on
things”: Unconscious thought leads to automatic weighting. Journal of Consumer Psychology 21:4–8. [aBRN]
Bos, M. W., Dijksterhuis, A. & van Baaren, R. B. (2008) On the goal-dependency of
unconscious thought. Journal of Experimental Psychology 44:1114–20. [AD]
Bowman, C. H., Evans, C. E. Y. & Turnbull, O. H. (2005) Artificial time constraints
on the Iowa Gambling Task: The effects on behavioural performance and
subjective experience. Brain and Cognition 57:21–25. [aBRN, HS, rDRS]
Boyer, J. L., Harrison, S. & Ro, T. (2005) Unconscious processing of orientation and
color without primary visual cortex. PNAS 102(46):16875–79. [BB]
Brehm, J. W. (1956) Post-decision changes in desirability of choice alternatives. Journal
of Abnormal and Social Psychology 52:384–89. doi: 10.1037/h0041006. [GC]
Bridgeman, B. & Staggs, D. (1982) Plasticity in human blindsight. Vision Research
22:1199–203. [BB]
Brigandt, I. (2005) The instinct concept of the early Konrad Lorenz. Journal of the
History of Biology 38:571–608. [JB]
Bröder, A. (2003) Decision making with the “Adaptive Toolbox”: Influence of
environmental structure, intelligence, and working memory load. Journal of
Experimental Psychology: Learning, Memory, and Cognition 29:611–25. [TR]
Bröder, A. & Schiffer, S. (2003a) Bayesian strategy assessment in multi-attribute
decision making. Journal of Behavioral Decision Making 16(3):193–213. doi:
10.1002/bdm.442. [HMH]
Bröder, A. & Schiffer, S. (2003b) Take the best versus simultaneous feature
matching: Probabilistic inferences from memory and effects of representation
format. Journal of Experimental Psychology: General 132(2):277–93. doi:
10.1037/0096–3445.132.2.277. [HMH]
Brogaard, B. (2011a) Are there unconscious perceptual processes? Consciousness
and Cognition 20:449–63. [BB]
Brogaard, B. (2011b) Color experience in blindsight? Philosophical Psychology
24:767–86. [BB]
Brogaard, B. (2011c) Conscious vision for action versus unconscious vision for action.
Cognitive Science 35:1076–104. [BB]
Brogaard, B. (2012a) Colour eliminativism or colour relativism? Philosophical Papers
41:305–21. [BB]
Brogaard, B. (2012b) Non-visual consciousness and visual images in blindsight.
Consciousness and Cognition 21:595–96. [BB]
Brooks, L. R., Norman, G. R. & Allen, S. W. (1991) The role of specific similarity in a
medical diagnostic task. Journal of Experimental Psychology: General, 120:278–
87. [aBRN]
Brooks, L. R. & Vokey, J. R. (1991) Abstract analogies and abstracted grammars:
Comments on Reber (1989) and Mathews et al. (1989) Journal of Experimental
Psychology: General 120:316–23. [aBRN]
Brooks, S. J., Savov V., Allzen, E., Benedict, C., Fredriksson, R. & Schioth, H. B. (2012)
Exposure to subliminal arousing stimuli induces robust activation in the amygdala,
hippocampus, anterior cingulate, insular cortex and primary visual cortex: A systematic meta-analysis of fMRI studies. NeuroImage 59:2962–73. [SJB]
Brunswik, E. (1952) The conceptual framework of psychology. University of Chicago
Press. [aBRN, CGV, JB]
Calvillo, D. P. & Penaloza, A. (2009) Are complex decisions better left to the
unconscious? Further failed replications of the deliberation-without-attention
effect. Judgment and Decision Making 4:509–17. [aBRN]
Cameron, C. D., Brown-Iannuzzi, J. & Payne, B. K. (2012) Sequential priming
measures of implicit social cognition: A meta-analysis of associations with
behaviors and explicit attitudes. Personality and Social Psychology Review
16:330–50. [AH]
54
Campbell-Meiklejohn, D. K., Bach, D. R., Roepstorff, A., Dolan, R. J. & Frith, C. D.
(2010) How the opinion of others affects our valuation of objects. Current
Biology 20(13):1165–70. [KH]
Campion, J., Latto, R. & Smith, Y. M. (1983) Is blindsight an effect of scattered light,
spared cortex, and near-threshold vision? Behavioral and Brain Sciences 6:423–
86. [BB, aBRN]
Carter, C. S. & van Veen, V. (2007) Anterior cingulate cortex and conflict detection:
An update of theory and data. Cognitive, Affective, and Behavioral Neuroscience 7:367–79. [SJB]
Carter, T. J., Ferguson, M. J. & Hassin, R. R. (2011) A single exposure to the
American flag shifts support toward Republicanism up to 8 months later.
Psychological Science 22(8) 1011–18. doi: 10.1177/0956797611414726. [RRH]
Cella, M., Dymond, S., Cooper, A. & Turnbull, O. (2007) Effects of decision-phase
time constraints on emotion-based learning in the Iowa Gambling Task. Brain
and Cognition 64:164–69. [aBRN, HS, rDRS]
Chaiken, S. & Trope, Y., eds. (1999) Dual-process theories in social psychology.
Guilford Press. [KH]
Chalmers, D. J. (1996) The conscious mind. Oxford University Press. [NP]
Chen, K. M. & Risen, J. L. (2010) How choice affects and reflects preferences:
Revisiting the free-choice paradigm. Journal of Personality and Social Psychology 99:573–94. doi: 10.1037/a0020217. [GC]
Churchland, P. M. (1988) Matter and consciousness (rev. ed.). MIT Press. [LA]
Cialdini, R. B. & Goldstein, N. J. (2004) Social influence: Compliance and conformity. Annual Review of Psychology 55:591–21. [KH]
Cleeremans, A. & Jiménez, L. (2002) Implicit learning and consciousness: A graded,
dynamic perspective. In: Implicit learning and consciousness, ed. R. M. French
& A. Cleeremans, pp. 1–40. Psychology Press. [LW]
Cook, R. L. & Stewart, T. R. (1975) A comparison of seven different methods of
obtaining subjective descriptions of judgmental policy. Organizational Behavior
and Human Performance 13:31–45. [TR]
Coppin, G., Delplanque, S., Cayeux, I., Porcherot, C. & Sander, D. (2010) I’m no longer
torn after choice: How explicit choices can implicitly shape preferences for odors.
Psychological Science 21:489–93. doi: 10.1177/0956797610364115. [GC, rDRS]
Costafreda, S. G., Brammer, M. J., David, A. S. & Fu, C. H. (2008) Predictors of
amygdala activation during the processing of emotional stimuli: A meta-analysis
of 385 PET and fMRI studies. Brain Research Reviews 58:57–70. [SJB]
Cowan, N. (2000) The magical number 4 in short-term memory: A reconsideration
of mental storage capacity. Behavioral and Brain Sciences 24:87–114. [RO]
Cowey, A. (2010) The blindsight saga. Experimental Brain Research 200:3–24. [aBRN]
Crandall, B. & Getchell-Reiter, K. (1993) Critical decision method: A technique for
eliciting concrete assessment indicators from the “intuition” of NICU nurses.
Advances in Nursing Sciences 16:42–51. [aBRN]
Creswell, D., Bursley, J. & Satpute, A. (2013) Neural reactivation links unconscous
thought to decision making performance. Social Cognitive and Affective
Neuroscience 8:863–69. [AD]
Cunningham, W. A., Nezlek, J. B. & Banaji, M. R. (2004) Conscious and unconscious
ethnocentrism: Revisiting the ideologies of prejudice. Personality and Social
Psychology Bulletin 30:1332–46. [ELU]
Cunningham, W. A., Preacher, K. J. & Banaji, M. R. (2001) Implicit attitude
measures: Consistency, stability, and convergent validity. Psychological Science
12:163–70. [ELU]
Damasio, A. (2010) Self comes to mind: Constructing the conscious brain. Random
House. [SJB]
Danziger, S., Levav, J. & Avnaim-Pesso, L. (2011a) Extraneous factors in judicial
decisions. Proceedings of the National Academy of Sciences USA 108(17):6889–
92. [KH, RMH]
Danziger, S., Levav, J. & Avnaim-Pesso, L. (2011b) Reply to Weinshall-Margel and
Shapard: Extraneous factors in judicial decisions persist. Proceedings of the
National Academy of Sciences USA 108(42):E834. [KH]
Davidson, W. P. (1983) The third-person effect in communication. Public Opinion
Quarterly 47:1–15. [EGH]
Davis, T., Love, B. C. & Maddox, W. T. (2009) Anticipatory emotions in decision
tasks: Covert markers of value or attentional processes? Cognition 112:195–200.
[aBRN]
Dawes, R. M. (1979) The robust beauty of improper linear models in decision
making. American Psychologist 34(7):571–82. [CGV]
Dawes, R. M., Faust, D. & Meehl, P. E. (1989) Clinical versus actuarial judgment.
Science 243(4899):1668–74. [aBRN, TR]
Dawson, M. E. & Reardon, P. (1973) Construct validity of recall and recognition
postconditioning measures of awareness. Journal of Experimental Psychology
98:308–15. [aBRN]
Dayan, E. & Bar-Hillel, M. (2011) Nudge to nobesity II: Menu positions influence
food orders. Judgment and Decision Making 6:333–42. [aBRN]
De Houwer, J. (2009) How do people evaluate objects? A brief review. Social and
Personality Psychology Compass 3:36–48. [AH]
De Martino, B., Harrison, N. A., Knafo, S., Bird, G. & Dolan, R. J. (2008) Explaining
enhanced logical consistency during decision making in autism. Journal of
Neuroscience 28(42):10746–50. [KH]
De Martino, B., Kumaran, D., Seymour, B. & Dolan, R. J. (2006) Frames, biases,
and rational decision-making in the human brain. Science 313(5787):684–87.
[KH]
Debner, J. & Jacoby, L. L. (1994) Unconscious perception: Attention, awareness,
and control. Journal of Experimental Psychology: Learning, Memory, and
Cognition 20:304–17. [NP, MS]
DeCoster, J. & Claypool, H. M. (2004) A meta-analysis of priming effects on
impression formation supporting a general model of informational biases. Personality and Social Psychology Review 8:2–27. [ELU]
Dehaene, S. (1997) The number sense. Oxford University Press. [RO]
Dehaene, S., Changeux, J., Naccache, L., Sackur, J. & Sergent, C. (2006) Conscious,
preconscious, and subliminal processing: A testable taxonomy. Trends in Cognitive Sciences 10:204–11. [AD, LW]
Dell’Acqua, R. & Grainger, J. (1999) Unconscious semantic priming from pictures.
Cognition 73(1):1–15. [MF]
DeWall, C. N., Baumeister, R. F. & Masicampo, E. J. (2008) Evidence that logical
reasoning depends on conscious processing. Consciousness and Cognition 17
(3):628–45. [RFB]
Dhami, M. K., Hertwig, R. & Hoffrage, U. (2004) The role of representative design
in an ecological approach to cognition. Psychological Bulletin 130:959–88.
[aBRN]
Dickinson, A. (1985) Actions and habits: The development of behavioural autonomy.
Philosophical Transactions of the Royal Society of London B308:67–78. [rDRS]
Dienes, Z. & Seth, A. (2010) Gambling on the unconscious: A comparison of
wagering and confidence ratings as measures of awareness in an artificial
grammar task. Consciousness and Cognition 19:674–81. [aBRN]
Dijksterhuis, A. (2004) Think different: The merits of unconscious thought in preference development and decision making. Journal of Personality and Social
Psychology 87:586–98. [aBRN, LW, rDRS]
Dijksterhuis, A. & Aarts, H. (2010) Goals, attention, and (un)consciousness. Annual
Review of Psychology 61:467–90. doi: 10.1146/annurev.psych.093008.100445.
[AD, LW]
Dijksterhuis, A., Bos, M. W., Nordgren, L. F., & van Baaren, R. B. (2006a) Complex
choices better made unconsciously? Science, 313, 760–61. [AD]
Dijksterhuis, A., Bos, M. W., Nordgren, L. F. & van Baaren, R. B. (2006b) On
making the right choice: The deliberation-without-attention effect. Science 311
(5763):1005–1007. doi: 10.1126/science.1121629. [aBRN, HMH, LW, rDRS]
Dijksterhuis, A., Bos, M. W., van der Leij, A. & van Baaren, R. (2009) Predicting
soccer matches after unconscious and conscious thought as a function of
expertise. Psychological Science 20:1381–87. [aBRN]
Dijksterhuis, A. & Nordgren, L. F. (2006) A theory of unconscious thought. Perspectives in Psychological Science 1:95–109. [aBRN, CGV, NS, LW, rDRS]
Dijksterhuis, A., Spears, R., Postmes, T., Stapel, D. A., Koomen, W., van Knippenberg, A. & Scheepers, D. (1998) Seeing one thing and doing another:
Contrast effects in automatic behavior. Journal of Personality and Social Psychology 75:862–71. [aBRN]
Dixon, N. F. (1971) Subliminal perception: The nature of a controversy. McGrawHill. [aBRN]
Douglas, K. M. & Sutton, R. M. (2008) The hidden impact of conspiracy theories:
Perceived and actual influence of theories surrounding the death of Princess
Diana. Journal of Social Psychology 148:210–21. [EGH]
Douglas, K. M. & Sutton, R. M. (2010) Right about others, wrong about ourselves?
Actual and perceived self-other differences in resistance to persuasion. British
Journal of Social Psychology 43:585–03. [EGH]
Doyen, S., Klein, O., Pichon, C.-L. & Cleeremans, A. (2012) Behavioral priming: It’s
all in the mind, but whose mind? PLoS One 7:e29081. [aBRN]
Duijvenvoorde, A. C. K. Van, Jansen, B. R. J., Visser, I. & Huizenga, H. M. (2010)
Affective and cognitive decision-making in adolescents. Developmental Neuropsychology 35(5):539–54. doi: 10.1080/875656412010494749. [HMH]
Dunn, B. D., Dalgleish, T. & Lawrence, A. D. (2006) The somatic marker hypothesis: A critical evaluation. Neuroscience and Biobehavioral Reviews 30:239–71.
[aBRN]
Dunn, B. D., Galton, H. C., Morgan, R., Evans, D., Oliver, C., Meyer, M. Cusack,
R., Lawrence, A. D. & Dalgleish, T. (2011) Listening to your heart: How
interoception shapes emotion experience and intuitive decision making.
Psychological Science 21:1835–44. [IPLM, rDRS]
Dunton, B. C. & Fazio, R. H. (1997) An individual difference measure of motivation
to control prejudiced reactions. Personality and Social Psychology Bulletin
23:316–26. [ELU]
Durgin, F. H., Baird, J. A., Greenburg, M., Russell, R., Shaughnessy, K. & Waymouth, S. (2009) Who is being deceived? The experimental demands of wearing
a backpack. Psychonomic Bulletin & Review 16:964–968. [aBRN]
Edelson, M., Sharot, T., Dolan, R. J. & Dudai, Y. (2011) Following the crowd: Brain
substrates of long-term memory conformity. Science 333(6038):108–11. [KH]
Edwards, W. & Fasolo, B. (2001) Decision technology. Annual Review of Psychology
52:581–606. [aBRN]
Egan, L. C., Bloom, P. & Santos, L. R. (2010) Choice-induced preferences in the
absence of choice: Evidence from a blind two-choice paradigm with young
children and capuchin monkeys. Journal of Experimental Social Psychology 46:
204–207. doi: 10.1016/j.jesp.2009.08.014. [GC]
Einhorn, H. J. & Hogarth, R. M. (1986) Judging probable cause. Psychological
Bulletin 99:3–19. [RMH]
Engle, R. (2002) Working memory capacity as executive attention. Current Directions in Psychological Science 11:19–23. [RO]
Epley, N. & Dunning, D. (2000) Feeling “holier than thou”: Are self-serving
assessments produced by errors in self- or social prediction? Journal of Personality and Social Psychology 79:861–75. [EGH]
Epstein, S. (1994) Integration of the cognitive and the psychodynamic unconscious.
American psychologist 49(8):709–24. [CGV]
Epstein, S. (2003) Cognitive-experiential self-theory of personality. In: Comprehensive handbook of psychology. Vol. 5: Personality and social psychology, ed.
T. Millon & M. J. Lerner, pp. 159–84. Wiley. [CGV]
Erb, H., Bioy, A. & Hilton, D. J. (2002) Choice preferences without inferences:
Subconscious priming of risk attitudes. Journal of Behavioral Decision Making
15:251–62. [ELU]
Ericsson, K. A. & Simon, H. A. (1980) Verbal reports as data. Psychological Review
87:215–51. [aBRN]
Evans, C. E. Y., Bowman, C. H. & Turnbull, O. H. (2005) Subjective awareness on
the Iowa Gambling Task: The key role of emotional experience in schizophrenia. Journal of Clinical and Experimental Neuropsychology 27:656–64.
[aBRN, rDRS]
Evans, J. St. B. T. (2007) Hypothetical thinking: Dual processes in reasoning and
judgement. Psychology Press. [JSBTE]
Evans, J. St. B. T. (2008) Dual-processing accounts of reasoning, judgment and social
cognition. Annual Review of Psychology 59:255–78. [aBRN, JSBTE]
Evans, J. St. B. T. (2010) Thinking twice: Two minds in one brain. Oxford University
Press. [JSBTE]
Evans, J. St. B. T., Clibbens, J., Cattani, A., Harris, A. & Dennis, I. (2003) Explicit and
implicit processes in multicue judgment. Memory & Cognition 31:608–18. [aBRN]
Evans, J. St. B. T. & Stanovich, K. E. (2013) Dual-process theories of higher cognition:
Advancing the debate. Perspectives on Psychological Science 8:223–41. [JSBTE]
Fazio, R. H., Jackson, J. R., Dunton, B. C. & Williams, C. J. (1995) Variability in
automatic activation as an unobtrusive measure of racial attitudes: A bona fide
pipeline? Journal of Personality and Social Psychology 69:1013–27. [AH, ELU]
Ferguson, M. (2008) On becoming ready to pursue a goal you don’t know you have:
Effects of nonconscious goals on evaluative readiness. Journal of Personality
and Social Psychology 95(6):1268–94. doi: 10.1037/a0013263. [RRH]
Ferguson, M. J. & Zayas, V. (2009) Automatic evaluation. Current Directions in
Psychological Science 18:362–66. [AH]
Fernandez-Seara, M. A., Aznarez-Sanado, M., Mengual, E., Loayza, F. R. & Pastor,
M. A. (2009) Continuous performance of a novel motor sequence leads to
highly correlated striatal and hippocampal perfusion increases. NeuroImage
47(4):1797–808. [JB]
Festinger, L. (1957) A theory of cognitive dissonance. Row Peterson and Stanford
University Press. [GC, AH]
Festinger, L. & Walster, E. (1964) Postdecision regret and decision reversal. In:
Conflict, decision, and dissonance, ed. L. Festinger, pp. 100–12. Stanford
University Press. [GC]
Finkbeiner, M. (2011) Subliminal priming with nearly perfect performance in the
prime-classification task. Attention, Perception, and Psychophysics 73(4):1255–
65. [rDRS, MF]
Finkbeiner, M. & Caramazza, A. (2008) Modulating the masked congruence priming
effect with the hands and the mouth. Journal of Experimental Psychology:
Human Perception and Performance 34:894–18. [MF]
Finkbeiner, M., Forster, K. I., Nicol, J. & Nakamura, K. (2004) The role of polysemy
in masked semantic and translation priming. Journal of Memory and Language
51 1–22. [MF]
Finkbeiner, M. & Palermo, R. (2009) The role of spatial attention in nonconscious
processing a comparison of face and nonface stimuli. Psychological Science 20
(1):42–51. [MF]
Fisk, G. D. & Haase, S. J. (2006) Exclusion failure does not demonstrate unconscious
perception II: Evidence from a forced-choice exclusion task. Vision Research
46:4244–51. [rDRS]
Fisk, G. D. & Haase, S. J. (2007) Exclusion failure does not demonstrate unconscious
perception. American Journal of Psychology 120:173–204. [rDRS]
Fisk, G. D. & Haase, S. (2011) The relationship between the objective identification
threshold and priming effects does not provide a definitive boundary between
conscious and unconscious perceptual processes. Consciousness and Cognition
20:1221–31. [MS]
Fridberg, D. J., Queller, S., Ahn, W.-Y., Kim, W., Bishara, A. J., Busemeyer, J. R.,
Porrino, L. & Stout, J. C. (2010) Cognitive mechanisms underlying risky
decision-making in chronic cannabis users. Journal of Mathematical Psychology
54:28–38. [HS]
Fried, I., Mukamel, R. & Kreiman, G. (2011) Internally generated preactivation of
single neurons in human medial frontal cortex predicts volition. Neuron 69:548–
62. [aBRN]
55
Gámez, E., Díaz, J. M. & Marrero, H. (2011) The uncertain universality of the Macbeth
effect with a Spanish sample. Spanish Journal of Psychology 14:156–62. [aBRN]
Garcia-Lazaro, H. G., Ramirez-Carmona, R., Lara-Romero, R., & Roldan-Valadez,
E. (2012) Neuroanatomy of episodic and semantic memory in humans: A brief
review of neuroimaging studies. Neurology India 60:613–17. [SJB]
Gavanski, I. & Hoffman, C. (1987) Awareness of influences on one’s own judgments:
The roles of covariation detection and attention to the judgment process.
Journal of Personality and Social Psychology 52:453–63. [aBRN]
Gawronski, B. & Bodenhausen, G. V. (2006) Associative and propositional processes
in evaluation: An integrative review of implicit and explicit attitude change.
Psychological Bulletin 132:692–731. [AH]
Gawronski, B. & Bodenhausen, G. V. (2011) The associative-propositional evaluation
model: Theory, evidence, and open questions. Advances in Experimental Social
Psychology 44:59–127. [AH]
Gawronski, B., Deutsch, R. & Banse, R. (2011) Response interference tasks as indirect
measures of automatic associations. In: Cognitive methods in social psychology,
ed. K. C. Klauer, A. Voss, & C. Stahl, pp. 78–123. Guilford Press. [AH]
Gawronski, B., Hofmann, W. & Wilbur, C. J. (2006) Are “implicit” attitudes
unconscious? Consciousness and Cognition 15:485–99. [AH]
Gawronski, B. & LeBel, E. P. (2008) Understanding patterns of attitude change:
When implicit measures show change, but explicit measures do not. Journal of
Experimental Social Psychology 44:1355–61. [AH]
Gawronski, B., Peters, K. R., Brochu, P. M. & Strack, F. (2008) Understanding the
relations between different forms of racial prejudice: A cognitive consistency
perspective. Personality and Social Psychology Bulletin 34:648–65. [AH]
Gawronski, B. & Strack, F. (2004) On the propositional nature of cognitive consistency: Dissonance changes explicit, but not implicit attitudes. Journal of
Experimental Social Psychology 40:535–42. [AH]
Gigerenzer, G. (2007) Gut feelings: The intelligence of the unconscious. Viking Press.
[aBRN]
Gigerenzer, G. & Gaissmaier, W. (2011) Heuristic decision making. Annual Review
of Psychology 62:451–82. [NS]
Gladwell, M. (2005) Blink: The power of thinking without thinking. Penguin. [aBRN]
Glöckner, A. & Betsch, T. (2008) Multiple-reason decision making based on automatic processing. Journal of experimental psychology: Learning, memory, and
cognition 34:1055–75. [aBRN]
Glöckner, A. & Witteman, C. (2010) Beyond dual-process models: A categorisation
of processes underlying intuitive judgement and decision making. Thinking &
Reasoning 16:1–25. [aBRN]
Gold, J. I. & Shadlen, M. N. (2007) The neural basis of decision making. Annual
Review of Neuroscience 30:535–74. [aBRN]
Goldstein, D. G. & Gigerenzer, G. (2002) Models of ecological rationality: The
recognition heuristic. Psychological Review 109:75–90. [aBRN]
González-Vallejo, C., Lassiter, G. D., Bellezza, F. S. & Lindberg, M. J. (2008) “Save
angels perhaps”: A critical examination of unconscious thought theory and the
deliberation-without-attention effect. Review of General Psychology 12(3):282–
96. [aBRN, CGV]
González-Vallejo, C. & Phillips, N. (2010) Predicting soccer matches: A reassessment
of the benefit of unconscious thinking. Judgment and Decision Making 5:200–
206. [aBRN]
Grainger, J., Diependaele, K., Spinelli, E., Ferrand, L. & Farioli, F. (2003) Masked
repetition and phonological priming within and across modalities. Journal of
Experimental Psychology: Learning, Memory, and Cognition 29:1256–69. [MF]
Green, D. M. & Swets, J. A. (1966) Signal detection theory and psychophysics, vol.
1974. Wiley. [MF, MS]
Greenwald, A. G. (1992) New look 3: Unconscious cognition reclaimed. American
Psychologist 47:766–79. [CGV]
Greenwald, A. G., McGhee, D. E. & Schwartz, J. K. L. (1998) Measuring individual
differences in implicit cognition: The Implicit Association Test. Journal of
Personality and Social Psychology 74:1464–80. [AH, ELU]
Guerin, B. & Innes, J. M. (1981) Awareness of cognitive processes: Replications and
revisions. Journal of General Psychology 104:173–89. [aBRN]
Guillaume, S., Jollant, F., Jaussent, I., Lawrence, N., Malafosse, A. & Courtet, P.
(2009) Somatic markers and explicit knowledge are both involved in decisionmaking. Neuropsychologia 47:2120–24. [IPLM, rDRS]
Gutbrod, K., Kroužel, C., Hofer, H., Müri, R., Perrig, W. & Ptak, R. (2006)
Decision-making in amnesia: Do advantageous decisions require conscious
knowledge of previous behavioural choices? Neuropsychologia 44:1315–24.
[aBRN, rDRS]
Hahn, A., Judd, C. M., Hirsh, H. K. & Blair, I. V. (in press) Awareness of implicit
attitudes. Journal of Experimental Psychology: General. [AH]
Haidt, J. (2001) The emotional dog and its rational tail: A social intuitionist approach
to moral judgment. Psychological Review 108:814–34. [GPDI]
Haidt, J. (2007) The new synthesis in moral psychology. Science 316:998–1002.
[GPDI, rDRS]
Ham, J. & van den Bos, K. (2010a) On unconscious morality: The effects of
unconscious thinking on moral decision making. Social Cognition 28:74–83.
[AD]
56
Ham, J. & van den Bos, K. (2010b) The merits of unconscious processing of directly
and indirectly obtained information about social justice. Social Cognition
28:180–90. [AD]
Ham, J. & van den Bos, K. (2011) On unconscious and conscious thought and
accuracy of implicit and explicit judgments. Social Cognition 29:648–67. [AD]
Ham, J., van den Bos, K. & van Doorn, E. (2009) Lady Justice thinks unconsciously:
Unconscious thought can lead to more accurate justice judgments. Social
Cognition 27:509–21. [aBRN, AD]
Hammond, K. R. (1955) Probabilistic functioning and the clinical method. Psychological Review 62(4):255–62. [CGV]
Hammond, K. R. (1986) A theoretically based review of theory and research in
judgment and decision making. University of Colorado. [CGV]
Hammond, K. R. (1996) Human judgment and social policy: Irreducible uncertainty,
inevitable error, unavoidable injustice. Oxford University Press. [aBRN, CGV]
Hammond, K. R. & Adelman, L. (1976) Science, values, and human judgment.
Science 194:389–96. [CGV]
Hammond, K. R., Hamm, R. M., Grassia, J. & Pearson, T. (1997) Direct comparison
of the efficacy of intuitive and analytical cognition in expert judgment. In:
Research on judgment and decision making: Currents, connections, and controversies, ed. W. M. Goldstein & R. M. Hogarth, pp. 144–80. Cambridge
University Press. [CGV]
Hammond, K. R. & Stewart, T. R., eds. (2001) The essential Brunswik: Beginnings,
explications, applications. Oxford University Press. [aBRN, CGV]
Handley, I. M. & Runnion, B. M. (2011) Evidence that unconscious thinking
influences persuasion based on argument quality. Social Cognition 29:668–82.
[AD]
Hanson, H. M. (1957) Discrimination training effect on stimulus generalization
gradient for spectrum stimuli. Science 125:888–89. [IPLM]
Hardin, C. L. (1985) Color for philosophers: Unweaving the rainbow (expanded ed.).
Hackett. [BB]
Harmon-Jones, E. & Mills, J., eds. (1999) Cognitive dissonance: Progress on a pivotal
theory in social psychology. American Psychological Association. [GC]
Harré, R. & Madden, E. H. (1975) Causal powers: A theory of natural necessity.
Blackwell. [aBRN]
Harries, C., Evans, J. St. B. T. & Dennis, I. (2000) Measuring doctors’ self-insight
into their treatment decisions. Applied Cognitive Psychology 14:455–77.
[aBRN, EGH]
Hassin, R. R. (2013) Yes it can: On the functional abilities of the human unconscious.
Perspectives in Psychological Science 8(2):195–207. [RRH]
Hassin, R. R., Bargh, J. A. & Zimerman, S. (2009) Automatic and flexible: The case of
non-conscious goal pursuit. Social cognition 27(1):20–36. Retrieved from http://
www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2659887&tool=pmcentrez&rendertype=abstract. [RRH]
Hassin, R. R., Ferguson, M. J., Shidlovski, D. & Gross, T. (2007) Subliminal exposure
to national flags affects political thought and behavior. Proceedings of the
National Academy of Sciences 104(50): 19757–61. [aBRN, MF]
Hassin, R. R., & Sklar, A. Y (in press). On unconscious processes and President
Obama. In: Dual process theories of the social mind. ed. J. W. Sherman, B.
Gawrosnki & Y. Trope. Guilford Press. [RRH]
Hastie, R. & Park, B. (1986) The relationship between memory and judgment
depends on whether the judgment task is memory-based or on-line. Psychological Review 93:258–68. [aBRN]
Helzer, E. G. & Dunning, D. (2013) Understanding agency in self and other: A metacognitive perspective. Unpublished manuscript. Wake Forest University.
[EGH]
Heywood, C. A. & Kentridge, R. W. (2003) Achromatopsia, colour vision & cortex.
Neurological Clinics of North America 21:483–500. [BB]
Heywood, C. A., Kentridge, R. W. & Cowey, A. (2001) Colour & the cortex:
Wavelength processing in cortical achromatopsia In: Varieties of unconscious
processing: New findings & models, ed. B. De Gelder, E. De Haan & C. A.
Heywood, pp. 52–68. Oxford University Press. [BB]
Higham, P. A. & Vokey, J. R. (2000) Judgment heuristics and recognition memory:
Prime identification and target-processing fluency. Memory & Cognition
28:574–84. [rDRS]
Hofmann, W., Gawronski, B., Gschwendner, T., Le, H. & Schmitt, M. (2005)
A meta-analysis on the correlation between the Implicit Association Test
and explicit self-report measures. Personality and Social Psychology Bulletin
31:1369–85. [AH]
Hogarth, R. M. (2001) Educating intuition. University of Chicago Press. [aBRN]
Hogarth, R. M. (2010) Intuition: A challenge for psychological research on decision
making. Psychological Inquiry 21:338–53. [aBRN]
Hogarth, R. M., Portell, M., Cuxart, A. & Kolev, G. I. (2011) Emotion and reason in
everyday risk perception. Journal of Behavioral Decision Making 24:202–22. [RMH]
Holender, D. (1986) Semantic activation without conscious identification in dichotic
listening, parafoveal vision, and visual masking: A survey and appraisal. Behavioral and Brain Sciences 9, 1–66. [aBRN]
Horstmann, A., Villringer, A. & Neumann, J. (2012) Iowa Gambling Task: There is
more to consider than long-term outcome. Using a linear equation model to
disentangle the impact of outcome and frequency of gains and losses. Frontiers
in Neuroscience6(May):61. doi: 10.3389/fnins.2012.00061. [HMH]
Huizenga, H. M., Crone, E. A. & Jansen, B. R. J. (2007) Decision-making in healthy
children, adolescents and adults explained by the use of increasingly complex
proportional reasoning rules. Developmental Science 10(6):814–25. doi:
10.1111/j.1467–7687.2007.00621.x. [HMH]
Huizenga, H. M., Wetzels, R., Van Ravenzwaaij, D. & Wagenmakers, E. J. (2012)
Four empirical tests of unconscious thought theory. Organizational Behavior
and Human Decision Processes 117(2):332–40. doi: 10.1016/j.
obhdp.2011.11.010. [aBRN, HMH]
Hurlburt, R. T. & Akhter, S. A. (2006) The descriptive experience sampling method.
Phenomenology and the Cognitive Sciences 5:271–301. [RO]
Ikomi, P. A. & Guion, R. M. (2000) The prediction of judgment in realistic tasks: An
investigation of self-insight. International Journal of Aviation Psychology
10:135–53. [aBRN]
Isaacson, W. (2007) Einstein: His life and universe. Simon & Schuster. [aBRN]
Jacoby, L. L. (1991) A process dissociation framework: Separating automatic from
intentional uses of memory. Journal of Memory and Language 30:513–41. [MS]
Jacoby, L. L., Lindsay, D. S. & Toth, J. P. (1992) Unconscious processes revealed:
Attention, awareness, and control. American Psychologist 47:802–809. [RRH]
James, W. (1890) The principles of psychology,vols. 1–2. Holt. [RFB, NP]
Jansen, B. R. J., Van Duijvenvoorde, A. C. K. & Huizenga, H. M. (2012) Development
of decision making: Sequential versus integrative rules. Journal of Experimental
Child Psychology 111(1):87–100. doi: 10.1016/j.jecp.2011.07.006. [HMH]
Johnstone, T. & Shanks, D. R. (2001) Abstractionist and processing accounts of
implicit learning. Cognitive Psychology 42:61–112. [aBRN]
Jones, F., & McLaren, I. P. L. (1999) Rules and associations. In: Proceedings of the
Twenty-First Annual Conference of the Cognitive Science Society, ed. M. Hahn
& S. C. Stoness, pp. 240–46. Erlbaum. [IPLM]
Jones, F. W. & McLaren, I. P. L. (2009) Human sequence learning under incidental
and intentional conditions. Journal of Experimental Psychology: Animal Behavior Processes 35:538–53. [IPLM]
Kahneman, D. (1973) Attention and Effort. Prentice-Hall. [RRH]
Kahneman, D. (2011) Thinking, fast and slow. Allen Lane and Farrar, Straus and
Giroux. [aBRN, SJB, JSBTE CGV, HMH, KH, NS]
Kahneman, D. & Frederick, S. (2007) Frames and brains: Elicitation and control of
response tendencies. Trends in Cognitive Sciences 11(2):45–46. [KH]
Kahneman, D. & Klein, G. (2009) Conditions for intuitive expertise: A failure to
disagree. American Psychologist 64:515–26. [aBRN]
Karelaia, N. & Hogarth, R. M. (2008) Determinants of linear judgment: A meta-analysis
of lens model studies. Psychological Bulletin 134(3):404–26. [aBRN, CGV]
Karremans, J. C., Stroebe, W. & Claus, J. (2006) Beyond Vicary’s fantasies: The
impact of subliminal priming and brand choice. Journal of Experimental Social
Psychology 42(6):792–98. [RRH]
Karmiloff-Smith, A. (1992) Beyond modularity: A developmental perspective on
cognitive science. MIT Press. [GPDI]
Kelley, H. & Friedman, D. (2002) Learning to forecast price. Economic Inquiry
40:556–73. [aBRN]
Keren, G. & Schul, Y. (2009) Two is not always better than one: A critical evaluation
of two-system theories. Perspectives on Psychological Science 4:533–50. [aBRN,
rDRS]
Kiesel, A., Wagener, A., Kunde, W., Hoffmann, J., Fallgatter, A. J. & Stöcker, C.
(2006) Unconscious manipulation of free choice in humans. Consciousness and
Cognition 15(2):397–408. doi: 10.1016/j.concog.2005.10.002. [RRH]
Klapp, S. T. & Haas, B. W. (2005) Nonconscious influence of masked stimuli on
response selection is limited to concrete stimulus–response associations.
Journal of Experimental Psychology: Human Perception and Performance 31
(1):193–209. doi: 10.1037/0096-1523.31.1.193. [RRH]
Klein, G. (1993) A recognition primed decision (RDP) model of rapid decision
making. In: Decision making in action: Models and methods, ed. G. Klein, J.
Oransu, R. Calderwood & C. Zsambok, pp. 138–47. Ablex. [aBRN]
Klein, O., Doyen, S., Leys, C., Magalhães de Saldanha da Gama, P A., Miller, S.,
Questienne, L. & Cleeremans, A. (2012) Low hopes, high expectations
expectancy effects and the replicability of behavioral experiments. Perspectives
on Psychological Science 7:572–84. [LW]
Klucharev, V., Hytönen, K., Rijpkema, M., Smidts, A. & Fernandez, G. (2009)
Reinforcement learning signal predicts social conformity. Neuron 61(1):140–51.
[KH]
Knudsen, E. (2007) Fundamental components of attention. Annual Review of
Neuroscience 30:57–78. [RO]
Koch, C. & Tsuchiya, N. (2006) Attention and consciousness: Two distinct brain
processes. Trends in Cognitive Sciences 11:16–22. [AD, LW]
Konstantinidis, E. & Shanks, D. R. (2013) Don’t bet on it! Wagering as a measure of
awareness in decision making under uncertainty. Manuscript submitted for
publication. [aBRN, rDRS]
Kouider, S. & Dehaene, S. (2007) Levels of processing during non-conscious perception: A critical review of visual masking. Philosophical Transactions of the
Royal Society B: Biological Sciences 362(1481):857–75. [SS, RRH]
Lagnado, D. A., Newell, B. R., Kahan, S. & Shanks, D. R. (2006) Insight and strategy
in multiple cue learning. Journal of Experimental Psychology: General 135:162–
83. [aBRN, IPLM]
Lamme, V. A. F. (2003) Why visual attention and awareness are different. Trends in
Cognitive Sciences 7:12–18. [AD]
Lassiter, D. G., Lindberg, M. J., González-Vallejo, C., Bellezza, F. S. & Phillips, N.
D. (2009) The deliberation-without-attention effect: Evidence for artifactual
interpretation. Psychological Science 20:671–75. [aBRN]
Lau, H. C. & Passingham, R. E. (2007) Unconscious activation of the cognitive
control system in the human prefrontal cortex. Journal of Neuroscience: The
Official Journal of the Society for Neuroscience 27(21):5805–11. doi: 10.1523/
JNEUROSCI.4335-06.2007. [RRH]
Le Doux, J. (1996) The emotional brain: The mysterious underpinnings of emotional
life. Touchstone. [SJB]
Lehrer, J. (2009) The decisive moment: How the brain makes up its mind. Text
Publishing. [aBRN]
Lerouge, D. (2009) Evaluating the benefits of distraction on product evaluations:
The mind-set effect. Journal of Consumer Research 36:367–79. [aBRN]
Leuthold, H. & Kopp, B. (1998) Mechanisms of priming by masked stimuli: Inferences from event-related brain potentials. Psychological Science 9:263–69. [RO]
Levin, I. P., Johnson, R. D., Russo, C. P. & Deldin, P. J. (1985) Framing effects in
judgment tasks with varying amounts of information. Organizational Behavior
and Human Decision Processes 36(3) 362–77. [KH]
Lewandowsky, S., Yang, L.-X., Newell, B. R. & Kalish, M. (2012) Working memory
does not dissociate between different perceptual categorization tasks. Journal
of Experimental Psychology: Learning, Memory, & Cognition 38:881–904.
[rDRS]
Li, Y. & Epley, N. (2009) When the best appears to be saved for last: Serial position
effects on choice. Journal of Behavioral Decision Making 22:378–89. [aBRN]
Libet, B. (1985) Unconscious cerebral initiative and the role of conscious will in
voluntary action. Behavioral and Brain Sciences 8:529–39. [aBRN, AD]
Libet, B., Gleason, C. A., Wright, E. W. & Pearl, D. K. (1983) Time of conscious
intention to act in relation to onset of cerebral-activity (readiness potential): The
unconscious initiation of a freely voluntary act. Brain 106:623–42. [aBRN]
Lieberman, M. D., Ochsner, K. N., Gilbert, D. T. & Schacter, D. L. (2001) Do
amnesiacs exhibit cognitive dissonance reduction? The role of explicit memory
and attention in attitude change. Psychological Science 2:135–40. doi: 10.1111/
1467–9280.00323. [GC]
Lin, C.-H., Chiu, Y.-C. & Huang, J.-T. (2009) Gain-loss frequency and final outcome
in the Soochow Gambling Task: A reassessment. Behavioral and Brain Functions 5:45. doi: 10.1186/1744-9081-5-45. [HMH]
Lin, C.-H., Song, T.-J., Lin, Y.-K. & Chiu, Y.-C. (2012) Mirrored prominent deck B
phenomenon: Frequent small losses override infrequent large gains in the
inverted Iowa Gambling Task. PloS One 7(10):e47202. doi: 10.1371/journal.
pone.0047202. [HMH]
Livesey, E. J. & McLaren, I. P. L. (2009) Discrimination and generalization along a
simple dimension: Peak shift and rule-governed responding. Journal of Experimental Psychology: Animal Behavior Processes 35:554–65. [IPLM]
Loersch, C., McCaslin, M. J. & Petty, R. E. (2011) Exploring the impact of social
judgeability concerns on the interplay of associative and deliberative attitude
processes. Journal of Experimental Social Psychology 47(5):1029–32. doi:
10.1016/j.jesp.2011.03.024. [RRH]
Loersch, C. & Payne, B. K. (2011) The situated inference model: An integrative
account of the effects of primes on perception, behavior, and motivation. Perspectives on Psychological Science 6:234–52. [aBRN]
Loewenstein, G. (1996) Out of control: Visceral influences on behavior. Organizational Behavior and Human Decision Processes 65(3):272–92. [KH]
Logan, G. D. & Zbrodoff, N. J. (1979) When it helps to be misled: Facilitative effects
of increasing the frequency of conflicting stimuli in a Stroop-like task. Memory
& Cognition 7:166–74. [aBRN]
Lombardi, W. J., Higgins, E. T. & Bargh, J. A. (1987) The role of consciousness in
priming effects on categorization: Assimilation versus contrast as a function of
awareness of the priming task. Personality and Social Psychology Bulletin
13:411–29. [ELU]
Lorenz, K. (1950) The comparative method in studying innate behaviour patterns.
In: Physiological mechanisms in animal behaviour, ed. J. F. Danielli & R.
Brown, pp. 221–68. Cambridge University Press. [JB]
Lovibond, P. F. & Shanks, D. R. (2002) The role of awareness in Pavlovian conditioning: Empirical evidence and theoretical implications. Journal of Experimental Psychology: Animal Behavior Processes 28:3–26. [aBRN, MS, rDRS]
Lyubomirsky, S. & Ross, L. (1999) Changes in attractiveness of elected, rejected, and
precluded alternatives: A comparison of happy and unhappy individuals. Journal
of Personality and Social Psychology 76:988–1007. doi: 10.1037/0022
3514.76.6.988. [GC]
Mackie, J. L. (1965) Causes and conditions. American Philosophical Quarterly
2:245–64. [RMH]
Mackie, J. L. (1974) The cement of the universe: A study of causation. Clarendon
Press. [RMH]
57
Macmillan, N. (1986) The psychophysics of subliminal perception. Behavioral and
Brain Sciences 9:38–39. [MS]
Macmillan, N. A. & Creelman, C. D. (2004) Detection theory: A user’s guide.
Erlbaum. [MF]
Maia, T. V. & McClelland, J. L. (2004) A re-examination of the evidence for the
somatic marker hypothesis: What participants really know in the Iowa Gambling
Task. Proceedings of the National Academy of Sciences USA 102:16075–80.
[aBRN, IPLM, RO, HS, rDRS]
Mamede, S., Schmidt, H. G., Rikers, R. M. J. P., Custers, E. J. F. M., Splinter, T.
A. W. & van Saase, J. L. C. M. (2010) Conscious thought beats deliberation
without attention in diagnostic decision-making: At least when you are an
expert. Psychological Research 74:586–92. [aBRN, rDRS]
Mantonakis, A., Rodero, P., Lesschaeve, I. & Hastie, R. (2009) Order in choice:
Effects of serial position on preferences. Psychological Science 20:1309–12.
[aBRN]
Marcus, G. F., Vijayan, S., Bandi Rao, S. & Vishton, P. M. (1999) Rule learning by
seven-month-old infants. Science 283:77–80. [aBRN]
McClure, J. (2012) Attributions, causes, and actions: Is the consciousness of will a
perceptual illusion? Theory & Psychology 22:402–19. doi: 10.1177/
0959354310386845. [aBRN]
McDowell, J. (2004) Naturalism in the philosophy of mind. In: Naturalism in
question, ed. M. de Caro & D. Macarthur, pp. 91–105. Harvard University
Press. [LA]
McElree, B. (2006) Accessing recent events. In: The psychology of learning and
motivation, vol. 46, ed. B. H. Ross, pp. 155–200. Academic Press. [NS]
McGregor, I., Newby-Clark, I. R. & Zanna, M. P. (1999) “Remembering” dissonance: Simultaneous accessibility of inconsistent cognitive elements moderates
epistemic discomfort. In: Cognitive dissonance: Progress on a pivotal theory in
social psychology, ed. E. Harmon-Jones & J. Mills, pp. 25–42. American
Psychological Association. [GC]
McLaren, I. P. L., Green, R. E. A. & Mackintosh, N. J. (1994) Animal learning and
the implicit/explicit distinction. In: Implicit and explicit learning of languages,
ed. N. C. Ellis, pp. 313–32. Academic Press. [IPLM]
McMahon, K., Sparrow, B., Chatman, L. & Riddle, T. (2011) Driven to distraction:
The impact of distracter type on unconscious decision making. Social Cognition
29:683–98. [AD, LW, rDRS]
Meehl, P. E. (1954) Clinical vs. statistical prediction. University of Minnesota Press.
[aBRN]
Meneguzzo, P., Tsakiris M., Schiöth, H. B., & Stein, D. J. (under review) Subliminal
versus supraliminal arousing stimuli indicate distinct neural activation in
anterior cingulate, insular cortex and inferior parietal lobe. [SJB]
Messner, C. & Wänke, M. (2011) Unconscious information processing reduces
information overload and increases product satisfaction. Journal of Consumer
Psychology 21:9–13. [AD]
Miller, J. (2000) Measurement error in subliminal perception experiments: Simulation analyses of two regression methods. Journal of Experimental Psychology:
Human Perception and Performance 26:1461–77. [aBRN]
Miller, J., Shepherdson, P. & Trevena, J. (2011) Effects of clock monitoring on
electroencephalographic activity: Is unconscious movement initiation an artifact
of the clock? Psychological Science 22:103–109. [aBRN]
Milyavsky, M., Hassin, R. R. & Schul, Y. (2012) Guess what? Implicit motivation
boosts the influence of subliminal information on choice. Consciousness and
Cognition 21(3):1232–41. doi: 10.1016/j.concog.2012.06.001. [RRH]
Mobbs, D., Petrovic, P., Marchant, J. L., Hassabis, D., Weiskopf, N. Seymour, B.,
Dolan, R. J., & Frith, C. D. (2007) When fear is near: Threat imminence elicits
prefrontal-periaqueductal gray shifts in humans. Science 317:1079–83. [SJB]
Monahan, J. L., Murphy, S. T. & Zajonc, R. B. (2000) Subliminal mere exposure:
Specific, general, and diffuse effects. Psychological Science 11(6):462–66.
[RRH]
Montague, P. R. & Lohrenz, T. (2007) To detect and correct: Norm violations and
their enforcement. Neuron 56(1): 14–18. [KH]
Morey, R. D., Rouder, J. N. & Speckman, P. L. (2008) A statistical model for discriminating between subliminal and near-liminal performance. Journal of
Mathematical Psychology 52:21–36. [MF]
Morey, R. D., Rouder, J. N. & Speckman, P. L. (2009) A truncated-probit item
response model for estimating psychophysical thresholds. Psychometrika
74:603–18. [MF]
Mudrik, L., Breska, A., Lamy, D. & Deouell, L. Y. (2011) Integration without
awareness. Psychological Science 22(6):764–70. doi: 10.1177/
0956797611408736. [RRH]
Mukherjee, S. & Srinivasan, N. (2013) Attention in preferential choice. Progress in
Brain Research 202:117–34. [NS]
Murillo, J. I. (2011) Is there room for the will in neuroscientific research? In: Moral
behaviour and free will, ed. J. J. Sanguineti, A. Acerbi & J. A. Lombo, pp. 61–79.
IF Press. [JB]
Mussweiler, T. & Englich, B. (2005) Subliminal anchoring: Judgmental consequences and underlying mechanisms. Organizational Behavior and Human
Decision Processes 98(2):133–43. doi: 10.1016/j.obhdp.2004.12.002. [RRH]
58
Mussweiler, T., Rüter, K. & Epstude, K. (2004) The man who wasn’t there: Subliminal social comparison standards influence self-evaluation. Journal of
Experimental Social Psychology 40(5):689–96. doi: 10.1016/j.jesp.2004.01.004.
[RRH]
Naccache, L. & Dehaene, S. (2001) Unconscious semantic priming extends to novel
unseen stimuli. Cognition 80:215–29. [MF]
Nahmias, E. (2005) Agency, authorship, and illusion. Consciousness and Cognition
14:771–85. [aBRN]
Newell, B. R. & Bright, J. E. H. (2002) Well past midnight: Calling time on implicit
invariant learning? European Journal of Cognitive Psychology 14:185–205.
[aBRN]
Newell, B. R. & Dunn, J. C. (2008) Dimensions in data: Testing psychological
models using state-trace analysis. Trends in Cognitive Sciences 12:285–90.
[rDRS]
Newell, B. R., Dunn, J. C. & Kalish, M. (2010) The dimensionality of perceptual
category learning: A state-trace analysis. Memory & Cognition: 38:563–81.
[rDRS]
Newell, B. R., Lagnado, D. A. & Shanks, D. R. (2007a) Challenging the role of
implicit processes in probabilistic category learning. Psychonomic Bulletin &
Review 14:505–11. [rDRS]
Newell, B. R., Lagnado, D. A. & Shanks, D. R. (2007b) Straight choices: The psychology of decision making. Psychology Press. [aBRN]
Newell, B. R. & Lee, M. D. (2011) The right tool for the job? Comparing an evidence
accumulation and a naïve strategy selection model of decision making. Journal
of Behavioral Decision Making 24:456–81. [rDRS]
Newell, B. R. & Rakow, T. (2011) Revising beliefs about the merits of unconscious
thought: Evidence in favor of the null hypothesis. Social Cognition 29:711–26.
[aBRN]
Newell, B. R., Wong, K. Y., Cheung, J. C. & Rakow, T. (2009) Think, blink or sleep
on it? The impact of modes of thought on complex decision making. Quarterly
Journal of Experimental Psychology 62:707–32. [aBRN, AD, rDRS]
Newman, L. S. & Uleman, J. S. (1990) Assimilation and contrast effects in spontaneous trait inference. Personality and Social Psychology Bulletin 16:224–40.
[ELU]
Nieuwenstein, M. & van Rijn, H. (2012) The unconscious thought advantage:
Further replication failures from a search for confirmatory evidence. Judgment
and Decision Making 7:779–98. [rDRS]
Nisbett, R. E. & Wilson, T. D. (1977) Telling more than we can know: Verbal
reports on mental processes. Psychological Review 84(3):231–59. [aBRN, LA,
rDRS]
Nolan, J. M., Schultz, P. W., Cialdini, R. B., Goldstein, N. J. & Griskevicius, V. (2008)
Normative social influence is underdetected. Personality and Social Psychology
Bulletin 34:913–23. [EGH]
Nordgren, L. F., Bos M. W. & Dijksterhuis, A. (2011) The best of both worlds:
Integrating conscious and unconscious thought best solves complex decisions.
Journal of Experimental Social Psychology 47:509–11. [aBRN]
Nosek, B. A. (2005) Moderators of the relationship between implicit and explicit
evaluation. Journal of Experimental Psychology: General 134:565–84. [AH,
ELU]
Olson, M. A., Fazio, R. H. & Hermann, A. D. (2007) Reporting tendencies underlie
discrepancies between implicit and explicit measures of self-esteem. Psychological Science 18:287–91. [ELU]
Opstal, F. Van, Lange, F. de & Dehaene, S. (2011) Rapid parallel semantic processing of numbers without awareness. Cognition 120:136–47. Retrieved from
http://www.sciencedirect.com/science/article/pii/S0010027711001053. [RRH]
Overgaard, M. (2011) Visual experience and blindsight: A methodological review.
Experimental Brain Research 209:473–79. [aBRN, BB, rDRS]
Overgaard, M., Fehl, K., Mouridsen, K., Bergholt, B. & Cleeremans, A. (2008)
Seeing without seeing? Degraded conscious vision in a blindsight patient. PLoS
One 3:e3028. [aBRN, rDRS]
Pachur, T. & Forer, E. A. (2013) Selection of decision strategies after conscious and
unconscious thought. Journal of Behavioral Decision Making 26:477–88.
[rDRS]
Panksepp, J. (2005) Affective consciousness: Core emotional feelings in animals and
humans. Consciousness and Cognition 14:30–80. [SJB]
Panksepp, J. (2011a) Cross-species affective neuroscience decoding of the primal
affective experiences of humans and related animals. PLoS One 6:e21236. [SJB]
Panksepp, J. (2011b) The basic emotional circuits of mammalian brains: Do animals
have affective lives? Neuroscience and Biobehavioral Reviews 35:1791–804.
[SJB]
Pashler, H., Harris, C. & Coburn, N. (2011, September 15) Elderly-related words
prime slow walking. Retrieved May 30, 2012, from http://www.PsychFileDrawer.org/replication.php?attempt=MTU%3D. [aBRN]
Patry, M. W. (2008) Attractive but guilty: Deliberation and the physical attractiveness bias. Psychological Reports 102:727–33. [GPDI]
Payne, B. K. (2001) Prejudice and perception: The role of automatic and controlled
processes in misperceiving a weapon. Journal of Personality and Social Psychology 81:181–92. [ELU]
Payne, B. K., Cheng, C. M., Govorun, O. & Stewart, B. D. (2005) An inkblot for
attitudes: Affect misattribution as implicit measurement. Journal of Personality
and Social Psychology 89:277–93. [ELU]
Payne, J. W., Bettman, J. R. & Johnson, E. (1988) Adaptive Strategy Selection in
Decision making. Journal of Experimental Psychology: Learning, Memory, and
Cognition 14(3):534–52. [HMH]
Payne, J. W., Samper, A., Bettman, J. R. & Luce, M. F. (2008) Boundary conditions
on unconscious thought in complex decision making. Psychological Science 19
(11):1118–23. doi: 10.1111/j.1467–9280.2008.02212.x. [aBRN, HMH, rDRS]
Perloff, R. M. (1993) Third-person effect research 1983–1992: A review and synthesis. International Journal of Public Opinion Research 5:167–94. [EGH]
Perloff, R. M. (1999) The third-person effect: A critical review and synthesis. Media
Psychology 1:353–78. [EGH]
Persaud, N. & Cowey, A. (2008) Blindsight is unlike normal conscious vision: Evidence from an exclusion task. Consciousness and Cognition 17:1050–55. [NP]
Persaud, N. & McLeod, P. (2007) Wagering demonstrates subconscious processing
in a binary exclusion task. Consciousness and Cognition 17: 565–75. [NP, rDRS]
Persaud, N., McLeod, P. & Cowey, A. (2007) Post-decision wagering objectively
measures awareness. Nature Neuroscience 10:257–261. [aBRN]
Phaf, R. H. & Wolters, G. (1997) A constructivist and connectionist view on conscious
and nonconscious processes. Philosophical Psychology 10(3):287–307. [CGV]
Phelps, R. H. & Shanteau, J. (1978) Livestock judges: How much information can an
expert use? Organizational Behavior and Human Performance 21:209–219.
[aBRN]
Pizarro, D. A. & Bloom, P. (2003) The intelligence of the moral intuitions: Comment
on Haidt (2001). Psychological Review 110:193–96. [GPDI]
Pohl, R. F., Erdfelder, E., Hilbig, B. E., Liebke, L. & Stahlberg, D. (2013) Effort
reduction after self-control depletion: The role of cognitive resources in use of
simple heuristics. Journal of Cognitive Psychology 25(3):267–76. [HMH]
Pothos, E. M. (2007) Theories of artificial grammar learning. Psychological Bulletin
133:227–44. [aBRN]
Pratte, M. S. & Rouder, J. N. (2009) A task-difficulty artifact in subliminal priming.
Attention, Perception & Psychophysics 71(6):1276–83. [aBRN, MF, MS]
Prinz, J. (2012) The conscious brain: How attention engenders experience. Oxford
University Press. [RO]
Pronin, E., Berger, J. & Molouki, S. (2007) Alone in a crowd of sheep: Asymmetric
perceptions of conformity and their roots in an introspective illusion. Journal of
Personality and Social Psychology 92:585–95. [EGH]
Raio, C. M., Carmel, D., Carrasco, M. & Phelps, E. A. (2012) Nonconscious fear is
quickly acquired but swiftly forgotten. Current Biology: CB 22(12):R477–79.
doi: 10.1016/j.cub.2012.04.023. [RRH]
Rakow, T., Harvey, N. & Finer, S., (2003) Improving calibration without training:
The role of task information. Applied Cognitive Psychology 17:419–41. [TR]
Ramsøy, T. Z. & Overgaard, M. (2004) Introspection and subliminal perception.
Phenomenology and the Cognitive Sciences 3:1–23. [aBRN]
Ranganath, K. A., Smith, C. T. & Nosek, B. A. (2008) Distinguishing automatic and
controlled components of attitudes from direct and indirect measurement
methods. Journal of Experimental Social Psychology 44:386–96. [ELU]
Reed, N., McLeod, P. & Dienes, Z. (2010) Implicit knowledge and motor skill: What
people who know how to catch don’t know. Consciousness and Cognition 19:63–
76. [rDRS]
Rees, G., Frith, C. D. & Lavie, N. (1997) Modulating irrelevant motion perception
by varying attentional load in an unrelated task. Science 278:1616–19. [aBRN]
Reilly, B. A. & Doherty, M. E. (1989) A note on the assessment of self-insight in
judgment research. Organizational Behavior and Human Decision Processes
44:123–31. [aBRN, TR]
Reilly, B. A. & Doherty, M. E. (1992) The assessment of self-insight in judgment
policies. Organizational Behavior and Human Decision Processes 53:285–309.
[aBRN, TR]
Reingold, E. M. & Merikle, P. M. (1988) Using direct and indirect measures to study
perception without awareness. Perception & Psychophysics 44:563–75. [aBRN]
Renkewitz, F., Fuchs, H. & Fiedler, S. (2011) Is there evidence of publication bias in
JDM research? Judgment and Decision Making 60:870–81. [aBRN]
Rey, A., Goldstein, R. M. & Perruchet, P. (2009) Does unconscious thought improve
complex decision making? Psychological Research 73:372–79. [aBRN]
Reyna, V. F. (2012) A new intuitionism: Meaning, memory, and development in
fuzzy-trace theory [presidential address]. Judgment and Decision Making
7:332–59. [LW]
Ric, F. & Muller, D. (2012) Unconscious addition: When we unconsciously initiate
and follow arithmetic rules. Journal of Experimental Psychology: General
141:222–26. doi: 10.1037/a0024608. [LW]
Richardson, D. C., Spivey, M. J. & Hoover, M. A. (2009) How to influence choice by
monitoring gaze. In: Proceedings of the 31st Annual Conference of the Cognitive
Science Society, ed. N. Taatgen, H. van Rijn, J. Nerbonne & L. Schomaker, pp.
2244. Cognitive Science Society. [aBRN, rDRS]
Roiser, J. P., De Martino, B., Tan, G. C. Y., Kumaran, D., Seymour, B., Wood, N. W.
& Dolan, R. J. (2009) A genetically mediated bias in decision making driven by
failure of amygdala control. Journal of Neuroscience 29(18):5985–91. [KH]
Rolison, J. J., Evans, J. St. B. T., Walsh, C. R. & Dennis, I. (2011) The role of working
memory capacity in multiple-cue probability learning. Quarterly Journal of
Experimental Psychology 64:1494–514. [aBRN]
Rolls, E. (1999) The brain and emotion. Oxford University Press. [RO]
Rouder, J. N., Morey, R. D., Speckman, P. L. & Pratte, M. S. (2007) Detecting
chance: A solution to the null sensitivity problem in subliminal priming. Psychonomic Bulletin & Review 14:597–605. [aBRN, MF]
Rumelhart, D. E., McClelland, J. L. & The PDP Research Group (1986) Parallel
distributed processing: Explorations in the microstructure of cognition. Vol. 1:
Foundations. MIT Press. [CGV]
Ryle, G. (1949) The concept of mind. Hutchinson. [rDRS]
Schacter, D. L. (1992) Priming and multiple memory systems: Perceptual mechanisms of implicit memory. Journal of Cognitive Neuroscience 4:244–56. [RO]
Schlaghecken, F. & Eimer, M. (2004) Masked prime stimuli can bias “free” choices
between response alternatives. Psychonomic Bulletin & Review 11(3):463–68.
Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/15376796. [RRH]
Schultz, P. W., Nolan, J. M., Cialdini, R. B., Goldstein, N. J. & Griskevicius, V. (2007)
The constructive, destructive and reconstructive power of social norms.
Psychological Science 18(5):429–34. [KH]
Searle, J. R. (1992) The rediscovery of the mind. MIT Press. [NP]
Seger, C. A. & Spiering, B. J. (2011) A critical review of habit learning and the basal
ganglia. Frontiers in systems neuroscience 5:66. doi: 10.3389/fnsys.2011.00066.
[JB]
Shanks, D. R. (2006) Complex choices better made unconsciously? Science 313:760.
[aBRN, rDRS]
Shanks, D. R. & Berry, C. J. (2012) Are there multiple memory systems? Tests of
models of implicit and explicit memory. Quarterly Journal of Experimental
Psychology 65:1449–74. [rDRS]
Shanks, D. R. & St. John, M. F. (1994) Characteristics of dissociable human learning
systems. Behavioral and Brain Sciences 17:367–447. [aBRN]
Shanteau, J. (1992) Competence in experts: The role of task characteristics.
Organizational Behavior and Human Decision Processes 53:252–66. [aBRN]
Sharot, T., Velasquez, C. M. & Dolan, R. J. (2010) Do decisions shape preference?
Evidence from blind choice. Psychological Science 2:1231–35. doi: 10.1177/
0956797610379235. [GC, rDRS]
Sher, S. & McKenzie, C. R. M. (2006) Information leakage from logically equivalent
frames. Cognition 101:467–94. [rDRS]
Sher, S. & McKenzie, C. R. M. (2011) Levels of information: A framing hierarchy.
In: Perspectives on framing, ed. G. Keren, pp. 35–64. Psychology Press. [rDRS]
Simmons, J. P., Nelson, L. D. & Simonsohn, U. (2011) False-positive psychology:
Undisclosed flexibility in data collection and analysis allows presenting anything
as significant Psychological Science 22:1359–66. [aBRN, SS]
Simon, H. A. (1992) What is an explanation of behavior? Psychological Science
3:150–61. [aBRN]
Simons, D. J., Hannula, D. E., Warren, D. E. & Day, S. W. (2007) Behavioral,
neuroimaging and neuropsychological approaches to implicit perception. In:
Cambridge handbook of consciousness, ed. P. D. Zelazo, M. Moscovitch, & E.
Thompson, pp. 207–50. Cambridge University Press. [SS]
Sklar, A., Levy, N., Goldstein, A., Mandel, R., Maril, A. & Hassin, R. R. (2012)
Reading and doing arithmetic nonconsciously. Proceedings of the National
Academy of Sciences USA 109(48):19614–19. Retrieved from www.pnas.org/
cgi/doi/10.1073/pnas.1211645109. doi: 10.1073/pnas.1211645109. [RFB, RRH,
rDRS]
Sloman, S. A. (1996) The empirical case for two systems of reasoning. Psychological
Bulletin 119(1):3–22. [KH]
Slovic, P., Fleissner, D. & Bauman, W. S. (1972) Analyzing the use of information in
investment decision making: A methodological proposal. Journal of Business
45:283–301. [aBRN]
Slovic, P. & Lichtenstein, S. (1971) Comparison of Bayesian and regression
approaches to the study of information processing in judgment. Organizational
Behaviour and Human Performance 6:649–744. [aBRN, TR]
Slovic, P. & Peters, E. (2006) Risk perception and affect. Current Directions in
Psychological Science 15:322–25. [RMH]
Smith, C. T. & Nosek, B. A. (2011) Affective focus increases the concordance
between implicit and explicit attitudes. Social Psychology 42:300–13. [ELU]
Smith, E. R. & Miller, F. D. (1978) Limits on perception of cognitive processes: A
reply to Nisbett and Wilson. Psychological Review 85:355–62. [aBRN]
Snodgrass, M., Bernat, E. & Shevrin, H. (2004a) Unconscious perception:
A model-based approach to method and evidence. Perception & Psychophysics
66:846–67. [MS]
Snodgrass, M., Bernat, E. & Shevrin, H. (2004b) Unconscious perception at the
objective detection threshold exists. Perception & Psychophysics 66 888–95. [MS]
Snodgrass, M. & Shevrin, H. (2006) Unconscious inhibition and facilitation at the
objective detection threshold: Replicable and qualitatively different unconscious perceptual effects. Cognition 101:43–79. [MS]
Soon, C. S., Brass, M., Heinze, H. J. & Haynes, J. D. (2008) Unconscious determinants of free decisions in the human brain. Nature Neuroscience 11: 543–45.
[AD, rDRS]
59
Snodgrass, M., Shevrin, H., & Kopka, M. (1993a) The mediation of intentional
judgments by unconscious perceptions: The influences of task strategy, task
preference, word meaning, and motivation. Consciousness and Cognition
2:169–93. [MS]
Snodgrass, M., Shevrin, H., & Kopka, M. (1993b) Absolute inhibition is incompatible
with conscious perception. Consciousness and Cognition 2:204–209. [MS]
Speekenbrink, M. & Shanks, D. R. (2010) Learning in a changing environment.
Journal of Experimental Psychology: General 139:266–98. [aBRN]
Spiegel, R. & McLaren, I. P. L. (2006) Associative sequence learning in humans. Journal
of Experimental Psychology: Animal Behavior Processes 32(2):156–63. [IPLM]
Squire, L. R. (1992) Declarative and nondeclarative memory: Multiple brain systems
supporting learning and memory. Journal of Cognitive Neuroscience 4:232–43.
[SS]
Srinivasan, N. & Mukherjee, S. (2010) Attribute preference and selection in multiattribute decision making: Implications for unconscious and conscious thought.
Consciousness & Cognition 19:644–52. [NS]
Srinivasan, N., Mukherjee, S., Mishra, M. V. & Kesarwani, S. (2013) Evaluating the
role of attention in the context of unconscious thought theory: Differential
impact of attentional scope and load on preference and memory. Frontiers in
Psychology 4:37. [NS]
Stanovich, K. E. (2011) Rationality and the reflective mind. Oxford University Press.
[JSBTE]
Stanovich, K. E. & West, R. F. (2000) Individual differences in reasoning: Implications for the rationality debate? Behavioral and Brain Sciences 23(5):645–65;
discussion, 665–726. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/
11301544. [HMH]
Stanovich, K. E., West, R. F. & Toplak, M. E. (2011) The complexity of developmental predictions from dual process models. Developmental Review 31:103–
18. [GPDI]
Stein, D. J. (1997) Cognitive science and the unconscious. American Psychiatric
Press. [SJB]
Stein, D. J., Solms, M. & van Honk, J. (2006) The cognitive–affective neuroscience of
the unconscious. CNS Spectrums 11:580–83. [SJB]
Steingroever, H., Wetzels, R., Horstmann, A., Neumann, J. & Wagenmakers, E.-J.
(2013) Performance of healthy participants on the Iowa Gambling Task.
Psychological Assessment 25:180–93. [HS]
Stewart, T. R., Roebber, P. J. & Bosart, L. F. (1997) The importance of the task in
analyzing expert judgment. Organizational Behavior and Human Decision
Processes 69(3):205–19. [CGV]
Stoerig, P. (1993) Sources of blindsight. Science 261:493–94. [BB]
Stoerig, P. & Cowey, A. (1992) Wavelength discrimination in blindsight. Brain
115:425–44. [BB]
Strack, F., Schwarz, N., Bless, H., Kübler, A. & Wänke, M. (1993) Awareness of the
influence as a determinant of assimilation versus contrast. European Journal of
Social Psychology 23:53–62. [ELU]
Strahan, E. J., Spencer, S. J. & Zanna, M. P. (2002) Subliminal priming and persuasion: Striking while the iron is hot. Journal of Experimental Social Psychology
38(6):556–68. [RRH]
Strick, M., Dijksterhuis, A., Bos, M. W., Sjoerdma, A., van Baaren, R. B. &
Nordgren, L. F. (2011) A meta-analysis on unconscious thought effects. Social
Cognition 29:738–62. [aBRN, AD, LW, rDRS]
Strick, M., Dijksterhuis, A. & van Baaren, R. (2010) Unconscious-thought effects
take place off-line, not on-line. Psychological Science 21:484–88. [aBRN]
Surber, C. F. (1985) Measuring the importance of information in judgment: Individual differences in weighting ability and effort. Organizational Behavior and
Human Decision Processes 35:156–78. [aBRN]
Thaler, R. (1980) Toward a positive theory of consumer choice. Journal of Economic
Behavior & Organization 1(1):39–60. [KH]
Thorsteinson, T. J. & Withrow, S. (2009) Does unconscious thought outperform
conscious thought on complex decisions? A further examination. Judgment and
Decision Making 4:235–47. [aBRN]
Tomb, I., Hauser, M., Deldin, P. & Caramazza, A. (2002) Do somatic markers
mediate decisions on the gambling task? Nature Neuroscience 5:1103–104.
[aBRN]
Trevena, J. A. & Miller, J. (2002) Cortical movement preparation before and after a
conscious decision to move. Consciousness and Cognition 11:162–90. [aBRN]
Tversky, A. & Kahneman, D. (1981) The framing of decisions and the psychology of
choice. Science 211(4481):453–58. [KH]
Tversky, A. & Kahneman, D. (1992) Advances in prospect theory: Cumulative representation of uncertainty. Journal of Risk and Uncertainty 5(4):297–323. doi:
10.1007/BF00122574. [HMH]
Tversky, A. & Slovic, P. (1988) Contingent weighting in judgment and choice.
Psychological Review 95(3):371–84. [HMH]
Uhlmann, E. L., Pizarro, D. A. & Bloom, P. (2008) Varieties of social cognition.
Journal for the Theory of Social Behaviour 38:293–322. [aBRN, ELU]
Ursu, S., Stenger, V. A., Shear, M. K., Jones, M. R. & Carter, C. S. (2003) Overactive
action monitoring in obsessive-compulsive disorder: Evidence from functional
magnetic resonance imaging. Psychological Science 14:347–53. [SJB]
60
Usher, M., Russo, Z., Weyers, M., Brauner, R. & Zakay, D. (2011) The impact of the
mode of thought in complex decisions: Intuitive decisions are better. Frontiers
in Psychology 2(March):37. doi: 10.3389/fpsyg.2011.00037. [aBRN, AD, HMH,
NS, rDRS]
Van Boven, L., Loewenstein, G., Dunning, D. & Nordgren, L. (2013) Changing
places: Empathy gaps in emotional perspective taking. In Advances in experimental social psychology, vol. 48, ed. J. Olson & M. P. Zanna, pp. 117–71.
Elsevier. [EGH]
Van Boven, L., Loewenstein, G., Welch, E. & Dunning, D. (2012) The illusion of
courage in self-predictions: Mispredicting one’s own behavior in embarrassing
situations. Journal of Behavioral Decision Making 25:1–12. [EGH]
Van den Bussche, E., Van den Noortgate, W., Reynvoet, B., Bussche, E. Van Den &
Noortgate, W. Van Den. (2009) Mechanisms of masked priming: A metaanalysis. Psychological Bulletin 135(3):452–77. doi: 10.1037/a0015329. [RRH]
van Gaal, S., de Lange, F. & Cohen, M. (2012) The role of consciousness in cognitive
control and decision making. Frontiers in Human Neuroscience 6:121. doi:
10.3389/fnhum.2012.00121. [MS]
van Gaal, S., Lamme, V. A. F., Fahrenfort, J. J., & Ridderinkhof, K. R. (2011) Dissociable brain mechanisms underlying the conscious and unconscious control of
behavior. Journal of Cognitive Neuroscience 23(1):91–105. [AD]
van Gaal, S., Ridderinkhof, K. R., Fahrenfort, J. J., Scholte, H. S. & Lamme, V. A. F.
(2008) Frontal cortex mediates unconsciously triggered inhibitory control.
Journal of Neuroscience: The Official Journal of the Society for Neuroscience 28
(32):8053–62. doi: 10.1523/JNEUROSCI.1278-08.2008. [RRH]
Van Selst, M. & Merikle, P. M. (1993) Perception below the objective threshold?
Consciousness and Cognition 2:194–203. [MS]
Velmans, M. (1991a) Consciousness from a first-person perspective. Behavioral and
Brain Sciences 14(4):702–26. [MV]
Velmans, M. (1991b) Is human information processing conscious? Behavioral and
Velmans, M. (1993) Consciousness, causality and complementarity. Behavioral and
Velmans, M. (1996) Consciousness and the “causal paradox.” Behavioral and Brain
Sciences 19(3):537–42. [MV]
Velmans, M. (2009) Understanding consciousness, 2nd ed. Routledge/Psychology
Press. [MV]
Veltkamp, M., Custers, R. & Aarts, H. (2011) Motivating consumer behavior by
subliminal conditioning in the absence of basic needs: Striking even while the
iron is cold. Journal of Consumer Psychology 21(1):49–56. doi: 10.1016/j.
jcps.2010.09.011. [RRH]
Verwijmeren, T., Karremans, J. C., Stroebe, W. & Wigboldus, D. H. J. (2011) The
workings and limits of subliminal advertising: The role of habits. Journal of
Consumer Psychology 21(2):206–13. doi: 10.1016/j.jcps.2010.11.004. [RRH]
Vrieze, S. I. & Grove, W. M. (2009) Survey on the use of clinical and mechanical
prediction methods in clinical psychology. Professional Psychology: Research
and Practice 40:525–31. [aBRN]
Wagar, B. M. & Dixon, M. (2006) Affective guidance in the Iowa Gambling Task.
Cognitive Affective & Behavioral Neuroscience 6:277–90. [aBRN]
Wagenmakers, E.-J., Wetzels, R., Borsboom, D. & van der Maas, H. L. J. (2011)
Why psychologists must change the way they analyze their data: The case of psi.
Waroquier, L., Marchiori, D., Klein, O. & Cleeremans, A. (2009) Methodological
pitfalls of the unconscious thought paradigm. Judgment and Decision Making
4:601–10. [LW]
Waroquier, L., Marchiori, D., Klein, O. & Cleeremans, A. (2010) Is it better to think
unconsciously or to trust your first impression? Social Psychological and Personality Science 1:111–18. [aBRN, LW]
Watanabe, M., Kang, C., Yusuke, M., Ueno, K., Asamizuya, T., Tanaka, K. &
Logothetis, N. (2011) Attention but not awareness modulates the BOLD
signal in the human V1 during binocular suppression. Science 334:829–31.
[LW]
Wegner, D. M. (2004) Précis of The illusion of conscious will. Behavioral and Brain
Sciences 27:649–92. [aBRN, JB]
Wegner, D. M. & Bargh, J. A. (1998) Control and automaticity in social life. In: The
handbook of social psychology, ed. S. T. Fiske & D. T. Gilbert, pp. 446–96.
McGraw-Hill. [AD]
Wegner, D. M., Sparrow, B. & Winerman, L. (2004) Vicarious agency: Experiencing
control over the movements of others. Journal of Personality and Social Psychology 86:838–48. [aBRN]
Weiner, K. S. & Grill-Spector, K. (2012) The improbable simplicity of the fusiform
face area. Trends in Cognitive Sciences 16:251–54. [SJB]
Weinshall-Margel, K. & Shapard, J. (2011) Overlooked factors in the analysis of
parole decisions. Proceedings of the National Academy of Sciences USA 108(42):
E833. [KH]
Weisberg, R. W. (2006) Creativity: Understanding innovation in problem solving,
science, invention, and the arts. Wiley. [aBRN]
Weiskrantz, L. (1986) Blindsight: A case study and implications. Oxford University
Press. [aBRN]
Weiskrantz, L. (2009) Is blindsight just degraded normal vision? Experimental Brain
Research 192:413–16. [aBRN, BB]
Wheeler, S. C. & DeMarree, K. G. (2009) Multiple mechanisms of prime-to-behavior effects. Social and Personality Psychology Compass 3:566–81. [ELU]
White, P. (1980) Limitations on verbal reports of internal events: A refutation of
Nisbett and Wilson and of Bem. Psychological Review 87:105–12. [aBRN]
White, P. A. (1988) Knowing more about what we can tell: “Introspective access” and
causal report accuracy 10 years later. British Journal of Psychology 79:13–45.
[aBRN]
White, P. A. (1989) Evidence for the use of information about internal events to
improve the accuracy of causal reports. British Journal of Psychology 80:375–
82. [aBRN]
Wicklund, R. A. & Brehm, J. W. (1976) Perspectives on cognitive dissonance.
Erlbaum. [GC]
Wilson, T. D. (2002) Strangers to ourselves: Discovering the adaptive unconscious.
Belknap Press. [aBRN, JSBTE]
Wilson, T. D. & Brekke, N. (1994) Mental contamination and mental correction:
Unwanted influences on judgments and evaluations. Psychological bulletin 116
(1):117–42. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/8078969. [RRH]
Wilson, T. D., Dunn, D. S., Bybee, J. A., Hyman, D. B. & Rotondo, J. A. (1984)
Effects of analyzing reasons on attitude–behavior consistency. Journal of Personality and Social Psychology 47:5–16. [ELU]
Wilson, T. D., Dunn, D. S., Kraft, D. & Lisle, D. J. (1989) Introspection, attitude
change, and attitude-behavior consistency: The disruptive effects of explaining
why we feel the way we do. In: Advances in experimental social psychology, vol.
22, ed. L. Berkowitz, pp. 287–343. Academic Press. [ELU]
Wilson, T. D. & LaFleur, S. J. (1995) Knowing what you’ll do: Effects of analyzing
reasons on self-prediction. Journal of Personality and Social Psychology 68:21–
35. [ELU]
Wilson, T. D. & Nisbett, R. E. (1978) The accuracy of verbal reports about the
effects of stimuli on evaluations and behavior. Social Psychology 41:118–31.
[aBRN, NS]
Wilson, T. D. & Schooler, J. W. (1991) Thinking too much: Introspection can reduce
the quality of preferences and decisions. Journal of Personality and Social
Psychology 60:181–92. [aBRN, rDRS]
Winkielman, P., Berridge, K. C. & Wilbarger, J. L. (2005) Unconscious affective
reactions to masked happy versus angry faces influence consumption behavior
and judgments of value. Personality and Social Psychology Bulletin 31:121–35.
[aBRN]
Winkielman, P. & Schooler, J. W. (2011) Splitting consciousness: Unconscious,
conscious, and metaconscious processes in social cognition. European Review of
Social Psychology 22: 1–35. [SS]
Wong, P., Bernat, E., Snodgrass, M. & Shevrin, H. (2004) Event-related brain correlates of associative learning without awareness. International Journal of Psychophysiology 53:217–31. [MS]
Wright, P. & Rip, P. D. (1981) Retrospective reports on the causes of decisions.
Wu, T., Kansaku, K. & Hallet M. (2004) How self-initiated memorized movements
become automatic: A functional MRI study. Journal of Neurophysiology
91:1690–98. [JB]
Zaki, J., Schirmer, J. & Mitchell, J. P. (2011) Social influence modulates the neural
computation of value. Psychological Science 22(7):894–900. [KH]
Zedelius, C., Veling, H. & Aarts, H. (2012) When unconscious rewards boost cognitive task performance inefficiently: The role of consciousness in integrating
value and attainability information. Frontiers in Human Neuroscience Research
6:219. [AD]
Zhong, C.-B. & Liljenquist, K. (2006) Washing away your sins: Threatened morality
and physical cleansing. Science 313:1451–52. [aBRN]
Zihl, J. (1980) Blindsight: Improvement of visually guided eye movements by systematic practice in patients with cerebral blindness. Neuropsychologia 18:71–
77. [BB]
Zihl, J. & Werth, R. (1984) Contributions to the study of “blindsight” II: The role of
specific practice for saccadic localization in patients with postgeniculate visual
field defects. Neuropsychologia 22:13–22. [BB]
61

Unconscious influences on decision making

Transcription

Similar documents

clicktosave

File

Handout 7: Two Views of Gangsta Rap and Conscious Hip Hop

eq power point - desmarais - ATD SF East Bay

Register by writing to: graduadospsicologiauio@ups.edu.ec

Baumrind, D. (1966). Effects of Authoritative Parental Control on

Brain Smart Start: From Chaos to Calm

Media Kit - blindfold

Personality

Untitled